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Combining Pattern Classifiers: Methods and Algorithms
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Learning Kernel Classifiers
About Learning Kernel Classifiers An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Combining Pattern Classifiers: Methods and Algorithms / Edition 2 – Hardcover
A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes: Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering. Product DetailsISBN-13: 9781118315231 Publisher: Wiley Publication Date: 09-15-2014 Pages: 384 Product Dimensions: 6.20(w) x 9.40(h) x 1.20(d)About the Author Ludmila Kuncheva is a Professor of Computer Science at Bangor University, United Kingdom. She has received two IEEE Best Paper awards. In 2012, Dr. Kuncheva was awarded a Fellowship to the International Association for Pattern Recognition (IAPR) for her contributions to multiple classifier systems.Read an Excerpt Click to read or download
Face Recognition Using Multiple Classifier Fusion (Paperback)
Recently classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. There are numbers of different Decision Support System (DSS) that has developed to operate on the minimum input data set or the output data set to give the correct decision. A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the face recognition performance. In this book, a face recognition system has been developed by applying multi-classifier fusion on the output of the three different classification methods namely Artificial Neural Network, Genetic Algorithm and Euclidean distance measure based on the Principal Component Analysis dimensionality reduction technique. Experimental results and performance analysis show the comparison results between multi-classifier fusion based face recognition system with individual classifier performance. Face Recognition Using Multiple Classifier Fusion (Paperback)
Stochastic Modelling and Applied Probability: A Probabilistic Theory of Pattern Recognition (Series #31) (Hardcover)
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field. • Author: Luc Devroye,Laszlo Gy\\u0026ouml;rfi,Gabor Lugosi • ISBN:9780387946184 • Format:Hardcover • Publication Date:1997-02-20
Transactions on Computational Science and Computational Inte: Pattern Recognition and Computational Intelligence Techniques Using Matlab (Hardcover)
Chapter1: Dimensionality Reduction Techniques.- Chapter2: Linear classifier techniques.- Chapter3: Regression techniques. Chapter4: Probabilistic supervised classifier and unsupervised clustering.- Chapter5: Computational intelligence.- Chapter6: Statistical test in pattern recognition. • Author: E S Gopi • ISBN:9783030222727 • Format:Hardcover • Publication Date:2019-10-28
Nato Asi Subseries F:: Syntactic and Structural Pattern Recognition (Series #45) (Paperback)
Proceedings of the NATO Advanced Research Workshop on Syntactic and Structural Pattern Recognition, held in Barcelona-Sitges, Spain, October 23-25, 1986 Thirty years ago pattern recognition was dominated by the learning machine concept: that one could automate the process of going from the raw data to a classifier. The derivation of numerical features from the input image was not considered an important step. One could present all possible features to a program which in turn could find which ones would be useful for pattern recognition. In spite of significant improvements in statistical inference techniques, progress was slow. It became clear that feature derivation was a very complex process that could not be automated and that features could be symbolic as well as numerical. Furthennore the spatial relationship amongst features might be important. It appeared that pattern recognition might resemble language analysis since features could play the role of symbols strung together to form a word. This led. to the genesis of syntactic pattern recognition, pioneered in the middle and late 1960’s by Russel Kirsch, Robert Ledley, Nararimhan, and Allan Shaw. However the thorough investigation of the area was left to King-Sun Fu and his students who, until his untimely death, produced most of the significant papers in this area. One of these papers (syntactic recognition of fingerprints) received the distinction of being selected as the best paper published that year in the IEEE Transaction on Computers. Therefore syntactic pattern recognition has a long history of active research and has been used in industrial applications. • ISBN:9783642834646 • Format:Paperback • Publication Date:2011-12-14
Pattern Recognition and Classification : An Introduction (Hardcover)
This volume, both comprehensive and accessible, introduces all the key concepts in pattern recognition, and includes many examples and exercises that make it an ideal guide to an important methodology widely deployed in today’s ubiquitous automated systems. Introduction.- Classification.- Nonmetric Methods.- Statistical Pattern Recognition.- Supervised Learning.- Nonparametric Learning.- Feature Extraction and Selection.- Unsupervised Learning.- Estimating and Comparing Classifiers.- Projects • Author: Geoff Dougherty • ISBN:9781461453222 • Format:Hardcover • Publication Date:2012-10-29
