Herbrich, Learning Kernel Classifiers: Theory and Algorithms (026208306X)

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?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.

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	Science/Engineering Information Theory, Inference & Learning Algorithms David J. C. MacKay,«Information Theory, Inference & Learning Algorithms»Cambridge University Press \| ISBN:0521642981 \| PDF \| 10,2 Mb \| 550 pages \| 2002 YearInformation theory and inference, often taught separately, are here united in one ...
	Technical Information Theory, Inference & Learning Algorithms
	Technical Information Theory, Inference, and Learning Algorithms Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data m ...
	Programming Extending the Scalability of Linkage Learning Genetic Algorithms: Theory Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & PracticeSpringer \| 3540284591 \| 2005 \| PDF \| 1MB \| RS \| FFGenetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely applied ...
	Combining Pattern Classifiers: Methods and Algorithms Ludmila I. KunchevaCombining Pattern Classifiers: Methods and AlgorithmsWiley-Interscience \| ISBN: 0471210781 \| 2004 \| 350 pages \| PDF \| 2.8 MBMail sorting, medical test reading, military target recognition, signature verification, meteorol ...
	Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & Practice Extending the Scalability of Linkage Learning Genetic Algorithms: Theory & PracticeSpringer \| 3540284591 \| 2005 \| PDF \| 1MB \| RS \| FFGenetic algorithms (GAs) are powerful search techniques based on principles of evolution and widely app ...

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