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BT 34.016 360.812 Td /F1 15.0 Tf [(Linear Discriminant Analysis Tutorial)] TJ ET
BT 34.016 332.273 Td /F1 7.5 Tf [(Eventually, you will enormously discover a new experience and attainment by spending more cash. yet when? realize you endure that you require to get )] TJ ET
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BT 34.016 313.958 Td /F1 7.5 Tf [(understand even more just about the globe, experience, some places, next history, amusement, and a lot more? )] TJ ET
BT 34.016 295.801 Td /F1 7.5 Tf [(It is your no question own get older to play reviewing habit. in the middle of guides you could enjoy now is )] TJ ET
BT 385.436 295.801 Td /F1 7.5 Tf [(Linear Discriminant Analysis Tutorial)] TJ ET
BT 506.711 295.801 Td /F1 7.5 Tf [( below.)] TJ ET
BT 34.016 262.643 Td /F1 7.5 Tf [(Mathematics for Machine Learning)] TJ ET
BT 149.058 262.643 Td /F1 7.5 Tf [( Marc Peter Deisenroth 2020-04-23 Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, )] TJ ET
BT 34.016 253.486 Td /F1 7.5 Tf [(probability and statistics that are used in machine learning.)] TJ ET
BT 34.016 244.328 Td /F1 7.5 Tf [(Image Analysis and Recognition)] TJ ET
BT 141.146 244.328 Td /F1 7.5 Tf [( Aurélio Campilho 2020-06-19 This two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the )] TJ ET
BT 34.016 235.171 Td /F1 7.5 Tf [(17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020. The 54 full papers )] TJ ET
BT 34.016 226.013 Td /F1 7.5 Tf [(presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical )] TJ ET
BT 34.016 216.856 Td /F1 7.5 Tf [(sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of )] TJ ET
BT 34.016 207.698 Td /F1 7.5 Tf [(histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management. Due to the )] TJ ET
BT 34.016 198.541 Td /F1 7.5 Tf [(corona pandemic, ICIAR 2020 was held virtually only.)] TJ ET
BT 34.016 189.383 Td /F1 7.5 Tf [(Python Machine Learning)] TJ ET
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34.016 188.146 m 119.058 188.146 l S
BT 119.058 189.383 Td /F1 7.5 Tf [( Sebastian Raschka 2015-09-23 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive )] TJ ET
BT 34.016 180.226 Td /F1 7.5 Tf [(analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective )] TJ ET
BT 34.016 171.068 Td /F1 7.5 Tf [(strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust )] TJ ET
BT 34.016 161.911 Td /F1 7.5 Tf [(statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your )] TJ ET
BT 34.016 152.753 Td /F1 7.5 Tf [(data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and )] TJ ET
BT 34.016 143.596 Td /F1 7.5 Tf [(unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build )] TJ ET
BT 34.016 134.438 Td /F1 7.5 Tf [(neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how )] TJ ET
BT 34.016 125.281 Td /F1 7.5 Tf [(to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover )] TJ ET
BT 34.016 116.123 Td /F1 7.5 Tf [(hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve )] TJ ET
BT 34.016 106.966 Td /F1 7.5 Tf [(deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations )] TJ ET
BT 34.016 97.808 Td /F1 7.5 Tf [(operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a )] TJ ET
BT 34.016 88.651 Td /F1 7.5 Tf [(challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build )] TJ ET
BT 34.016 79.493 Td /F1 7.5 Tf [(sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine )] TJ ET
BT 34.016 70.336 Td /F1 7.5 Tf [(Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want )] TJ ET
BT 34.016 61.178 Td /F1 7.5 Tf [(to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is )] TJ ET
BT 34.016 52.021 Td /F1 7.5 Tf [(invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(demonstrating how to get to grips with a range of statistical models.)] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(Data Preparation for Machine Learning)] TJ ET
BT 163.241 341.558 Td /F1 7.5 Tf [( Jason Brownlee 2020-06-30 Data preparation involves transforming raw data in to a form that can be modeled using )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(to know to get the most out of your data on your next project. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(discover how to confidently and effectively prepare your data for predictive modeling with machine learning.)] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(Brain–Computer Interfaces Handbook)] TJ ET
BT 159.903 304.928 Td /F1 7.5 Tf [( Chang S. Nam 2018-01-09 Brain–Computer Interfaces Handbook: Technological and Theoretical Advances provides a )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(tutorial and an overview of the rich and multi-faceted world of Brain–Computer Interfaces \(BCIs\). The authors supply readers with a contemporary )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(presentation of fundamentals, theories, and diverse applications of BCI, creating a valuable resource for anyone involved with the improvement of people’s )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(lives by replacing, restoring, improving, supplementing or enhancing natural output from the central nervous system. It is a useful guide for readers interested )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(in understanding how neural bases for cognitive and sensory functions, such as seeing, hearing, and remembering, relate to real-world technologies. More )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(precisely, this handbook details clinical, therapeutic and human-computer interfaces applications of BCI and various aspects of human cognition and behavior )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(such as perception, affect, and action. It overviews the different methods and techniques used in acquiring and pre-processing brain signals, extracting )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(features, and classifying users’ mental states and intentions. Various theories, models, and empirical findings regarding the ways in which the human brain )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(interfaces with external systems and environments using BCI are also explored. The handbook concludes by engaging ethical considerations, open questions, )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(and challenges that continue to face brain–computer interface research. Features an in-depth look at the different methods and techniques used in acquiring )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(and pre-processing brain signals, extracting features, and classifying the user's intention Covers various theories, models, and empirical findings regarding )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(ways in which the human brain can interface with the systems or external environments Presents applications of BCI technology to understand various )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(aspects of human cognition and behavior such as perception, affect, action, and more Includes clinical trials and individual case studies of the experimental )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(therapeutic applications of BCI Provides human factors and human-computer interface concerns in the design, development, and evaluation of BCIs Overall, )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(this handbook provides a synopsis of key technological and theoretical advances that are directly applicable to brain–computer interfacing technologies and )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(can be readily understood and applied by individuals with no formal training in BCI research and development.)] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(Advances in Kernel Methods)] TJ ET
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BT 129.476 158.408 Td /F1 7.5 Tf [( Bernhard Schölkopf 1999 A young girl hears the story of her great-great-great-great- grandfather and his brother who came to )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(the United States to make a better life for themselves helping to build the transcontinental railroad.)] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(Practical Linear Algebra for Data Science)] TJ ET
BT 171.153 140.093 Td /F1 7.5 Tf [( Mike X Cohen 2022-09-06 If you want to work in any computational or technical field, you need to understand linear )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic \(various )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(multiplications and transformations\) Independence, rank, and inverses Important decompositions used in applied linear algebra \(including LU and QR\) )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis)] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(Machine Learning Paradigms: Theory and Application)] TJ ET
BT 212.426 57.676 Td /F1 7.5 Tf [( Aboul Ella Hassanien 2018-12-08 The book focuses on machine learning. Divided into three parts, the )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning \(ML\) is not new in the field )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.)] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(Discriminatory Analysis)] TJ ET
BT 111.521 341.558 Td /F1 7.5 Tf [( Evelyn Fix 1985 )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(Machine Learning Mastery With Python)] TJ ET
BT 164.898 332.401 Td /F1 7.5 Tf [( Jason Brownlee 2016-04-08 The Python ecosystem with scikit-learn and pandas is required for operational machine )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(directly to production. In this Ebook, learn exactly how to get started and apply machine learning using the Python ecosystem.)] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(Hybrid Intelligent Systems)] TJ ET
BT 120.708 304.928 Td /F1 7.5 Tf [( Ana Maria Madureira 2019-03-20 This book highlights recent research on Hybrid Intelligent Systems and their various practical )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(applications. It presents 56 selected papers from the 18th International Conference on Hybrid Intelligent Systems \(HIS 2018\), which was held at the Instituto )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(Superior de Engenharia do Porto \(ISEP\), Porto, Portugal from December 13 to 15, 2018. A premier conference in the field of Artificial Intelligence, HIS 2018 )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(brought together researchers, engineers and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(authors from over 30 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(Engineering.)] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(Sensor-Actuator Supported Implicit Interaction in Driver Assistance Systems)] TJ ET
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BT 286.601 249.983 Td /F1 7.5 Tf [( Andreas Riener 2011-06-07 Andreas Riener studies the influence of implicit )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(interaction using vibro-tactile actuators as additional sensory channels for car-driver feedback and pressure sensor arrays for implicit information transmission )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(from the driver toward the vehicle. The results of his experiments suggest the use of both vibro-tactile notifications and pressure sensor images to improve )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(vehicle handling performance and to decrease the driver’s cognitive workload.)] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(XGBoost With Python)] TJ ET
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34.016 212.116 m 106.548 212.116 l S
BT 106.548 213.353 Td /F1 7.5 Tf [( Jason Brownlee 2016-08-05 XGBoost is the dominant technique for predictive modeling on regular data. The gradient boosting )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(algorithm is the top technique on a wide range of predictive modeling problems, and XGBoost is the fastest implementation. When asked, the best machine )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(learning competitors in the world recommend using XGBoost. In this Ebook, learn exactly how to get started and bring XGBoost to your own machine learning )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(projects.)] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(XXVI Brazilian Congress on Biomedical Engineering)] TJ ET
BT 207.416 176.723 Td /F1 7.5 Tf [( Rodrigo Costa-Felix 2019-05-15 This volume presents the proceedings of the Brazilian Congress on )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(Biomedical Engineering \(CBEB 2018\). The conference was organised by the Brazilian Society on Biomedical Engineering \(SBEB\) and held in Armação de )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(Buzios, Rio de Janeiro, Brazil from 21-25 October, 2018. Topics of the proceedings include these 11 tracks: • Bioengineering • Biomaterials, Tissue )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(Engineering and Artificial Organs • Biomechanics and Rehabilitation • Biomedical Devices and Instrumentation • Biomedical Robotics, Assistive Technologies )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(and Health Informatics • Clinical Engineering and Health Technology Assessment • Metrology, Standardization, Testing and Quality in Health • Biomedical )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(Signal and Image Processing • Neural Engineering • Special Topics • Systems and Technologies for Therapy and Diagnosis)] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(Web Technologies Research and Development - APWeb 2005)] TJ ET
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34.016 120.541 m 242.021 120.541 l S
BT 242.021 121.778 Td /F1 7.5 Tf [( Yanchun Zhang 2005-03-22 This book constitutes the refereed proceedings of the 7th Asia-)] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(Pacific Web Conference, APWeb 2005, held in Shanghai, China in March/April 2005. The 71 revised full papers and 22 revised short papers presented )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(together with 6 keynote papers and 22 invited demo papers were carefully reviewed and selected from 420 submissions. The papers are organized in topical )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(sections on classification and clustering, topic and concept discovery, text search and document generation, Web search, mobile computing and P2P, XML, )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(integration and collaboration, data mining and analysis, Web browsing and navigation, spatial data, stream data processing, Web services, ontologies, change )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(management, personalization, performance and optimization, Web caching, data grid, multimedia, object recognition and information extraction, visualization )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(and user interfaces, and delivery and networks.)] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(Machine Learning for Audio, Image and Video Analysis)] TJ ET
BT 216.603 57.676 Td /F1 7.5 Tf [( Francesco Camastra 2015-07-21 This second edition focuses on audio, image and video data, the )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification \(automatically )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(assigning a data sample to one of the classes belonging to a predefined set\), clustering \(automatically grouping data samples according to the similarity of )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(their properties\) and sequence analysis \(automatically mapping a sequence of observations into a sequence of human-understandable symbols\). The third )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(available data and free software packages, thus allowing readers to replicate the experiments.)] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(Advances in Artificial Intelligence and Data Engineering)] TJ ET
BT 217.848 295.771 Td /F1 7.5 Tf [( Niranjan N. Chiplunkar 2020-08-13 This book presents selected peer-reviewed papers from the )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(International Conference on Artificial Intelligence and Data Engineering \(AIDE 2019\). The topics covered are broadly divided into four groups: artificial )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(artificial intelligence and data engineering.)] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(An Introduction to Statistical Learning)] TJ ET
BT 158.246 231.668 Td /F1 7.5 Tf [( Gareth James 2013-06-24 An Introduction to Statistical Learning provides an accessible overview of the field of )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(\(Hastie, Tibshirani and Friedman, 2nd edition 2009\), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(knowledge of matrix algebra.)] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(The Nature of Statistical Learning Theory)] TJ ET
BT 170.741 130.936 Td /F1 7.5 Tf [( Vladimir Vapnik 2013-06-29 The aim of this book is to discuss the fundamental ideas which lie behind the statistical )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(book is intended for statisticians, mathematicians, physicists, and computer scientists.)] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Applied MANOVA and Discriminant Analysis)] TJ ET
BT 181.556 85.148 Td /F1 7.5 Tf [( Carl J. Huberty 2006-05-12 A complete introduction to discriminant analysis--extensivelyrevised, expanded, and )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(updated This Second Edition of the classic book, AppliedDiscriminant Analysis, reflects and references current usagewith its new title, Applied MANOVA and )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(DiscriminantAnalysis. Thoroughly updated and revised, this book continuesto be essential for any researcher or student needing to learn tospeak, read, and )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(write about discriminant analysis as well asdevelop a philosophy of empirical research and data analysis. Itsthorough introduction to the application of )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(discriminant analysisis unparalleled. Offering the most up-to-date computer applications, references,terms, and real-life research examples, the Second )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(Editionalso includes new discussions of MANOVA, descriptive discriminantanalysis, and predictive discriminant analysis. Newer SAS macrosare included, and )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(graphical software with data sets and programsare provided on the book's related Web site. The book features: Detailed discussions of multivariate analysis of )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(variance andcovariance An increased number of chapter exercises along with selectedanswers Analyses of data obtained via a repeated measures design A )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(new chapter on analyses related to predictive discriminantanalysis Basic SPSS\(r\) and SAS\(r\) computer syntax and output integratedthroughout the book )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(Applied MANOVA and Discriminant Analysis enables thereader to become aware of various types of research questions usingMANOVA and discriminant )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(analysis; to learn the meaning of thisfield's concepts and terms; and to be able to design a study thatuses discriminant analysis through topics such as one-)] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(factorMANOVA/DDA, assessing and describing MANOVA effects, and deletingand ordering variables.)] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(Applied Predictive Modeling)] TJ ET
BT 126.551 314.086 Td /F1 7.5 Tf [( Max Kuhn 2013-05-17 Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-)] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.)] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(Object Detection by Stereo Vision Images)] TJ ET
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34.016 221.273 m 172.826 221.273 l S
BT 172.826 222.511 Td /F1 7.5 Tf [( R. Arokia Priya 2022-09-14 OBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(intelligent systems. Audience Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(retailers.)] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(Predicting Structured Data)] TJ ET
BT 121.968 103.463 Td /F1 7.5 Tf [( Neural Information Processing Systems Foundation 2007 State-of-the-art algorithms and theory in a novel domain of machine )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(learning,prediction when the output has structure.)] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Principles of Data Mining)] TJ ET
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34.016 83.911 m 116.958 83.911 l S
BT 116.958 85.148 Td /F1 7.5 Tf [( Max Bramer 2016-11-09 This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(from time to time - a phenomenon known as concept drift.)] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018)] TJ ET
BT 363.768 323.243 Td /F1 7.5 Tf [( Aboul Ella Hassanien 2018-08-28 This book presents the )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(proceedings of the 4th International Conference on Advanced Intelligent Systems and Informatics 2018 \(AISI2018\), which took place in Cairo, Egypt from )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(September 1 to 3, 2018. This international and interdisciplinary conference, which highlighted essential research and developments in the field of informatics )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(and intelligent systems, was organized by the Scientific Research Group in Egypt \(SRGE\). The book is divided into several main sections: Intelligent Systems; )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(Robot Modeling and Control Systems; Intelligent Robotics Systems; Machine Learning Methodology and Applications; Sentiment Analysis and Arabic Text )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(Mining; Swarm Optimizations and Applications; Deep Learning and Cloud Computing; Information Security, Hiding, and Biometric Recognition; and Data )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(Mining, Visualization and E-learning.)] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(An Introduction to Applied Multivariate Analysis with R)] TJ ET
BT 213.251 259.141 Td /F1 7.5 Tf [( Brian Everitt 2011-04-23 The majority of data sets collected by researchers in all disciplines are )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(multivariate techniques to multivariate data.)] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(The Elements of Statistical Learning)] TJ ET
BT 154.068 167.566 Td /F1 7.5 Tf [( Trevor Hastie 2013-11-11 During the past decade there has been an explosion in computation and information )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(book’s coverage is broad, from supervised learning \(prediction\) to unsupervised learning. The many topics include neural networks, support vector machines, )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(factorization, and spectral clustering. There is also a chapter on methods for “wide” data \(p bigger than n\), including multiple testing and false discovery rates. )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.)] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(Discriminant Analysis)] TJ ET
BT 105.273 369.031 Td /F1 7.5 Tf [( William R. Klecka 1980-08 Background. Deriving the canonical discriminant functions. Interpreting the canonical discriminant functions. )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(Classification procedures. Stepwise inclusion of variables. Concluding remarks.)] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(Guide to Vulnerability Analysis for Computer Networks and Systems)] TJ ET
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34.016 349.478 m 259.923 349.478 l S
BT 259.923 350.716 Td /F1 7.5 Tf [( Simon Parkinson 2018-09-04 This professional guide and reference examines the )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(challenges of assessing security vulnerabilities in computing infrastructure. Various aspects of vulnerability assessment are covered in detail, including recent )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(advancements in reducing the requirement for expert knowledge through novel applications of artificial intelligence. The work also offers a series of case )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(studies on how to develop and perform vulnerability assessment techniques using start-of-the-art intelligent mechanisms. Topics and features: provides )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(tutorial activities and thought-provoking questions in each chapter, together with numerous case studies; introduces the fundamentals of vulnerability )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(assessment, and reviews the state of the art of research in this area; discusses vulnerability assessment frameworks, including frameworks for industrial )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(control and cloud systems; examines a range of applications that make use of artificial intelligence to enhance the vulnerability assessment processes; )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(presents visualisation techniques that can be used to assist the vulnerability assessment process. In addition to serving the needs of security practitioners and )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(researchers, this accessible volume is also ideal for students and instructors seeking a primer on artificial intelligence for vulnerability assessment, or a )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(supplementary text for courses on computer security, networking, and artificial intelligence.)] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI)] TJ ET
BT 268.616 259.141 Td /F1 7.5 Tf [( Vivian Siahaan 2021-03-03 In this book, you will learn how to use NumPy, Pandas, )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(Adaline, Logistic Regression \(LR\), Support Vector Machine \(SVM\), Decision Tree \(DT\), Random Forest \(RF\), and K-Nearest Neighbor \(KNN\) models. You will )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(also learn how to extract features using Principal Component Analysis \(PCA\), Linear Discriminant Analysis \(LDA\), Kernel Principal Component Analysis )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(\(KPCA\) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(\(SIFT\), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test \(FAST\). In Chapter 4, In this tutorial, you will learn how to use )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline \(adaptive linear neuron\). The dataset used is Iris dataset )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(PyQt, Tutorial Steps To Implement Adaline \(ADAptive LInear NEuron\), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(classification using perceptron, Adaline \(adaptive linear neuron\), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine \(SVM\) Using Scikit-Learn, Tutorial Steps To )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(Implement Decision Tree \(DT\) Using Scikit-Learn, Tutorial Steps To Implement Random Forest \(RF\) Using Scikit-Learn, and Tutorial Steps To Implement K-)] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(Nearest Neighbor \(KNN\) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(collected in the modern age of technology. You will learn the following topics: Principal Component Analysis \(PCA\) for unsupervised data compression, Linear )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(Discriminant Analysis \(LDA\) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(Kernel Principal Component Analysis \(KPCA\). You will learn: 6.1 Tutorial Steps To Implement Principal Component Analysis \(PCA\), Tutorial Steps To )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(Implement Principal Component Analysis \(PCA\) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis \(PCA\) Using Scikit-Learn with )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(PyQt, Tutorial Steps To Implement Linear Discriminant Analysis \(LDA\), Tutorial Steps To Implement Linear Discriminant Analysis \(LDA\) with Scikit-Learn, )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(Tutorial Steps To Implement Linear Discriminant Analysis \(LDA\) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(Analysis \(KPCA\) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis \(KPCA\) Using Scikit-Learn with PyQt. In Chapter )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression \(LR\) Model With )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression \(LR\) Model With LDA Feature Extractor on MNIST )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression \(LR\) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(To Implement , Tutorial Steps To Implement Support Vector Machine \(SVM\) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(To Implement Support Vector Machine \(SVM\) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(\(DT\) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree \(DT\) Model With LDA Feature Extractor )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree \(DT\) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(Steps To Implement Random Forest \(RF\) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(\(RF\) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest \(RF\) Model With KPCA Feature )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor \(KNN\) Model With PCA Feature Extractor on MNIST Dataset )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor \(KNN\) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(Implement K-Nearest Neighbor \(KNN\) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt.)] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(Handbook of Statistical Analysis and Data Mining Applications)] TJ ET
BT 239.936 57.676 Td /F1 7.5 Tf [( Robert Nisbet 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(of novel analytical tools and techniques, and their practical applications)] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(Master Machine Learning Algorithms)] TJ ET
BT 156.138 295.771 Td /F1 7.5 Tf [( Jason Brownlee 2016-03-04 You must understand the algorithms to get good \(and be recognized as being good\) at )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-)] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(by-step.)] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(Methods of Multivariate Analysis)] TJ ET
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34.016 267.061 m 141.551 267.061 l S
BT 141.551 268.298 Td /F1 7.5 Tf [( Alvin C. Rencher 2003-04-14 Amstat News asked three review editors to rate their topfive favorite books in the September )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(2003 issue. Methods ofMultivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit,it is often )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(necessary to analyze the variables simultaneously,rather than isolate them and consider them individually.Multivariate analysis enables researchers to explore )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(the jointperformance of such variables and to determine the effect of eachvariable in the presence of the others. The Second Edition of AlvinRencher's )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(Methods of Multivariate Analysis provides studentsof all statistical backgrounds with both the fundamental and moresophisticated skills necessary to master )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(the discipline. To illustrate multivariate applications, the author providesexamples and exercises based on fifty-nine real data sets from awide variety of )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(scientific fields. Rencher takes a "methods"approach to his subject, with an emphasis on how students andpractitioners can employ multivariate analysis in )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(real-lifesituations. The Second Edition contains revised and updatedchapters from the critically acclaimed First Edition as well asbrand-new chapters on: )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(Cluster analysis Multidimensional scaling Correspondence analysis Biplots Each chapter contains exercises, with corresponding answers andhints in the )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(appendix, providing students the opportunity to testand extend their understanding of the subject. Methods ofMultivariate Analysis provides an authoritative )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(reference forstatistics students as well as for practicing scientists andclinicians.)] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(Applications of Artificial Intelligence for Smart Technology)] TJ ET
BT 224.921 167.566 Td /F1 7.5 Tf [( Swarnalatha, P. 2020-10-30 As global communities are attempting to transform into more efficient )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(and technologically-advanced metropolises, artificial intelligence \(AI\) has taken a firm grasp on various professional fields. Technology used in these )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(industries is transforming by introducing intelligent techniques including machine learning, cognitive computing, and computer vision. This has raised )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(significant attention among researchers and practitioners on the specific impact that these smart technologies have and what challenges remain. Applications )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(of Artificial Intelligence for Smart Technology is a pivotal reference source that provides vital research on the implementation of advanced technological )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(techniques in professional industries through the use of AI. While highlighting topics such as pattern recognition, computational imaging, and machine )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(learning, this publication explores challenges that various fields currently face when applying these technologies and examines the future uses of AI. This )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(book is ideally designed for researchers, developers, managers, academicians, analysts, students, and practitioners seeking current research on the )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(involvement of AI in professional practices.)] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Statistical Pattern Recognition)] TJ ET
BT 134.058 85.148 Td /F1 7.5 Tf [( Andrew R. Webb 2003-07-25 Statistical pattern recognition is a very active area of study andresearch, which has seen many )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-)] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a)] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(Dimension Reduction)] TJ ET
BT 105.288 304.928 Td /F1 7.5 Tf [( Christopher J. C. Burges 2010 Dimension Reduction: A Guided Tour covers many well-known, and some less well-known, methods for )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(dimension reduction for which the inferred variables are continuous. It describes the mathematics and key ideas underlying the methods, and provides some )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(links to the literature for those interested in pursuing a topic further)] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(In-Depth Tutorials: Deep Learning Using Scikit-Learn, Keras, and TensorFlow with Python GUI)] TJ ET
BT 347.883 277.456 Td /F1 7.5 Tf [( Vivian Siahaan 2021-06-05 BOOK 1: LEARN FROM )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(SCRATCH MACHINE LEARNING WITH PYTHON GUI In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression \(LR\), Support )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(Vector Machine \(SVM\), Decision Tree \(DT\), Random Forest \(RF\), and K-Nearest Neighbor \(KNN\) models. You will also learn how to extract features using )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(Principal Component Analysis \(PCA\), Linear Discriminant Analysis \(LDA\), Kernel Principal Component Analysis \(KPCA\) algorithms and use them in machine )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-)] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform \(SIFT\), and Tutorial Steps To Detect Features Using )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(Features from Accelerated Segment Test \(FAST\). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(classification using perceptron and Adaline \(adaptive linear neuron\). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline \(ADAptive LInear )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(NEuron\), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline \(adaptive linear neuron\), )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(Steps To Implement Support Vector Machine \(SVM\) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree \(DT\) Using Scikit-Learn, Tutorial Steps To )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(Implement Random Forest \(RF\) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor \(KNN\) Using Scikit-Learn. In Chapter 6, you will )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(topics: Principal Component Analysis \(PCA\) for unsupervised data compression, Linear Discriminant Analysis \(LDA\) as a supervised dimensionality reduction )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis \(KPCA\). You will learn: Tutorial Steps )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(To Implement Principal Component Analysis \(PCA\), Tutorial Steps To Implement Principal Component Analysis \(PCA\) Using Scikit-Learn, Tutorial Steps To )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(Implement Principal Component Analysis \(PCA\) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis \(LDA\), Tutorial Steps )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(To Implement Linear Discriminant Analysis \(LDA\) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis \(LDA\) Using Scikit-Learn with )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis \(KPCA\) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(Component Analysis \(KPCA\) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(Tutorial Steps To Implement Logistic Regression \(LR\) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(Logistic Regression \(LR\) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression \(LR\) Model With )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine \(SVM\) Model )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine \(SVM\) Model With KPCA Feature Extractor )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree \(DT\) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(Steps To Implement Decision Tree \(DT\) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree \(DT\) )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest \(RF\) Model With PCA Feature Extractor on )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest \(RF\) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(Steps To Implement Random Forest \(RF\) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(Neighbor \(KNN\) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor \(KNN\) Model With LDA )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor \(KNN\) Model With KPCA Feature Extractor on MNIST )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(Dataset Using PyQt. BOOK 2: THE PRACTICAL GUIDES ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(handwritten digits using MNIST dataset with PyQt. You will build a GUI application for this purpose. In Chapter 3, you will learn how to perform recognizing )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(learn how to perform detecting brain tumor using Brain Image MRI dataset provided by Kaggle \(https://www.kaggle.com/navoneel/brain-mri-images-for-brain-)] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(tumor-detection\) using CNN model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(dataset provided by Kaggle \(https://www.kaggle.com/cashutosh/gender-classification-dataset\) using MobileNetV2 and CNN models. You will build a GUI )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(\(https://www.kaggle.com/nicolejyt/facialexpressionrecognition\) using CNN model. You will also build a GUI application for this purpose. BOOK 3: STEP BY )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(STEP TUTORIALS ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use )] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI \(PyQt\). )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(\(SIFT\). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(using Fruits 360 dataset provided by Kaggle \(https://www.kaggle.com/moltean/fruits/code\) using Transfer Learning and CNN models. You will build a GUI )] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(classifying cats/dogs using dataset provided by Kaggle \(https://www.kaggle.com/chetankv/dogs-cats-images\) using Using CNN with Data Generator. You will )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(to perform detecting furnitures using Furniture Detector dataset provided by Kaggle \(https://www.kaggle.com/akkithetechie/furniture-detector\) using VGG16 )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle \(https://www.kaggle.com/zalando-)] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(research/fashionmnist/code\) using CNN model. You will build a GUI application for this purpose. BOOK 4: Project-Based Approach On DEEP LEARNING )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(Using Scikit-Learn, Keras, And TensorFlow with Python GUI In this book, implement deep learning on detecting vehicle license plates, recognizing sign )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(Detection dataset provided by Kaggle \(https://www.kaggle.com/andrewmvd/car-plate-detection/download\). In Chapter 2, you will learn how to use TensorFlow, )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(Kaggle \(https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download\). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(\(https://www.kaggle.com/arunrk7/surface-crack-detection/download\). BOOK 5: Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And )] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-)] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle )] TJ ET
BT 34.016 48.518 Td /F1 7.5 Tf [(\(https://www.kaggle.com/omkargurav/face-mask-dataset/download\). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle )] TJ ET
BT 34.016 369.031 Td /F1 7.5 Tf [(\(https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download\). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, )] TJ ET
BT 34.016 359.873 Td /F1 7.5 Tf [(Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle )] TJ ET
BT 34.016 350.716 Td /F1 7.5 Tf [(\(https://www.kaggle.com/alxmamaev/flowers-recognition/download\). BOOK 6: Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And )] TJ ET
BT 34.016 341.558 Td /F1 7.5 Tf [(TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and )] TJ ET
BT 34.016 332.401 Td /F1 7.5 Tf [(scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn )] TJ ET
BT 34.016 323.243 Td /F1 7.5 Tf [(how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey )] TJ ET
BT 34.016 314.086 Td /F1 7.5 Tf [(Species dataset provided by Kaggle \(https://www.kaggle.com/slothkong/10-monkey-species/download\). In Chapter 2, you will learn how to use TensorFlow, )] TJ ET
BT 34.016 304.928 Td /F1 7.5 Tf [(Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset )] TJ ET
BT 34.016 295.771 Td /F1 7.5 Tf [(provided by Kaggle \(https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download\). In Chapter 3, you will learn how to use TensorFlow, Keras, )] TJ ET
BT 34.016 286.613 Td /F1 7.5 Tf [(Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify airplane, car, and ship using Multiclass-image-dataset-airplane-car-ship )] TJ ET
BT 34.016 277.456 Td /F1 7.5 Tf [(dataset provided by Kaggle \(https://www.kaggle.com/abtabm/multiclassimagedatasetairplanecar\).)] TJ ET
BT 34.016 268.298 Td /F1 7.5 Tf [(Proceedings of the 5th International Conference on Signal Processing and Information Communications)] TJ ET
BT 377.928 268.298 Td /F1 7.5 Tf [( Chua-Chin Wang 2022-12-18 This book presents the )] TJ ET
BT 34.016 259.141 Td /F1 7.5 Tf [(proceedings of the 5th International Conference on Signal Processing and Information Communications \(ICSPIC\)\), which was held in Paris, France on March )] TJ ET
BT 34.016 249.983 Td /F1 7.5 Tf [(14-16, 2022. The conference solicits papers on all aspects of signal processing and information communications, which includes mixed signal processing, )] TJ ET
BT 34.016 240.826 Td /F1 7.5 Tf [(multimedia signal processing, nonlinear signal processing, communication theory and techniques, optical communications, and wireless networks. The )] TJ ET
BT 34.016 231.668 Td /F1 7.5 Tf [(conference is made up of theorists and experts in advanced characterization techniques in the fields of signal processing and information communications, )] TJ ET
BT 34.016 222.511 Td /F1 7.5 Tf [(which brings researchers, practitioners, and scientists in discussion of the latest methods, research developments, and future opportunities.)] TJ ET
BT 34.016 213.353 Td /F1 7.5 Tf [(Metric Learning)] TJ ET
BT 85.698 213.353 Td /F1 7.5 Tf [( Aurélien Muise 2022-05-31 Similarity between objects plays an important role in both human cognitive processes and artificial systems for )] TJ ET
BT 34.016 204.196 Td /F1 7.5 Tf [(recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern )] TJ ET
BT 34.016 195.038 Td /F1 7.5 Tf [(recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from )] TJ ET
BT 34.016 185.881 Td /F1 7.5 Tf [(data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric )] TJ ET
BT 34.016 176.723 Td /F1 7.5 Tf [(learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic )] TJ ET
BT 34.016 167.566 Td /F1 7.5 Tf [(metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting )] TJ ET
BT 34.016 158.408 Td /F1 7.5 Tf [(with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go )] TJ ET
BT 34.016 149.251 Td /F1 7.5 Tf [(beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more )] TJ ET
BT 34.016 140.093 Td /F1 7.5 Tf [(complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature )] TJ ET
BT 34.016 130.936 Td /F1 7.5 Tf [(on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical )] TJ ET
BT 34.016 121.778 Td /F1 7.5 Tf [(frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we )] TJ ET
BT 34.016 112.621 Td /F1 7.5 Tf [(illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information )] TJ ET
BT 34.016 103.463 Td /F1 7.5 Tf [(retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / )] TJ ET
BT 34.016 94.306 Td /F1 7.5 Tf [(Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / )] TJ ET
BT 34.016 85.148 Td /F1 7.5 Tf [(Bibliography / Authors' Biographies)] TJ ET
BT 34.016 75.991 Td /F1 7.5 Tf [(Pattern Classification)] TJ ET
BT 104.456 75.991 Td /F1 7.5 Tf [( Richard O. Duda 2012-11-09 The first edition, published in 1973, has become a classicreference in the field. Now with the second )] TJ ET
BT 34.016 66.833 Td /F1 7.5 Tf [(edition, readers willfind information on key new topics such as neural networks andstatistical pattern recognition, the theory of machine learning,and the theory )] TJ ET
BT 34.016 57.676 Td /F1 7.5 Tf [(of invariances. Also included are worked examples,comparisons between different methods, extensive graphics, expandedexercises and computer project )] TJ ET
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BT 34.016 378.188 Td /F1 7.5 Tf [(topics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.)] TJ ET
BT 36.266 349.792 Td /F1 8.0 Tf [(linear-discriminant-analysis-tutorial)] TJ ET
BT 330.910 350.000 Td /F1 8.0 Tf [(Downloaded from )] TJ ET
BT 395.822 349.792 Td /F1 8.0 Tf [(wagnerplein.nl)] TJ ET
BT 447.398 350.000 Td /F1 8.0 Tf [( on December 3, 2022 by guest)] TJ ET
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