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Learning Kernel Classifiers Theory by Herbrich Ralf (22 results)
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Condition: Good. [ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: NONE ] [ Writing: NONE ] [ Edition: First ] Publisher: The MIT Press Pub Date: 1/1/2002 Binding: Hardcover Pages: 366 First edition.
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Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)
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Condition: acceptable. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Acceptable condition! Any other included accessories are also in Acceptable condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear such as cover tears discoloration, staining, marks, scuffs, etc. All pages intact.
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Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)
Seller: Goodwill of Silicon Valley, SAN JOSE, CA, U.S.A.
Seller rating 4 out of 5 stars
Condition: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
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hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning series)
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paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning series)
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Learning Kernel Classifiers : Theory and Algorithms
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Learning Kernel Classifiers : Theory and Algorithms
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Add to basketPaperback. Condition: Brand New. 384 pages. 9.00x7.00x0.89 inches. In Stock.
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Learning Kernel Classifiers: Theory and Algorithms
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 209.
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 classifiera limited, but well-established and comprehensively studied modeland 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.
