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Learning Kernel Classifiers Theory by Herbrich Ralf (23 results)
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hardcover. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
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hardcover. Condition: Good. Ex-library book with typical stickers and stampings. Priority Mail is available on this item. No international shipping.
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hardcover. Condition: Very Good. Most of our very good books have only minor imperfections such as shelf wear consistent with a new book that's sat on a bookshop shelf for a year or two. Occasionally we may miss other minor imperfections as we have to grade books at speed. Our books are dispatched from a Yorkshire former cotton mill. We list via barcode/ISBN so please note that the images are stock images and may not be the exact copy you receive, furthermore the details about edition and year might not be accurate as many publishers reuse the same ISBN for multiple editions and as we simply scan a barcode or enter an ISBN we do not check the validity of the edition data when listing. If you're looking for an exact edition please don't order (at least not without checking with us first, although we don't always have time to check). We aim to dispatch prompty, the service used will depend on order value and book size. We can ship to most countries, see our shipping policies. Payment is via Abe only.
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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)
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
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Condition: Good. Good condition ex-library book with usual library markings and stickers.
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Hardcover. Condition: Very Good. Dust Jacket Condition: Fine. Second printing. Size: Medium (20 to 26cm). Item Type: Book. Text body is clean and unmarked. Binding tight, spine fine. ISBN: 026208306X. ISBN/EAN: 9780262083065. **Heavy Book. A Postage surcharge may be requested. Contact us BEFORE ordering for a quote. Click Ask Bookseller a Question** *** WE POST TO AUSTRALIA,UK,IRELAND,CANADA,USA,NEW ZEALAND,JAPAN & SINGAPORE ONLY ***.
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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 (Adaptive Computation and Machine Learning series)
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Learning Kernel Classifiers : Theory and Algorithms
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Paperback. Condition: Brand New. 384 pages. 9.00x7.00x0.89 inches. In Stock.
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Learning Kernel Classifiers: Theory and Algorithms
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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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.
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Taschenbuch. Condition: Neu. Learning Kernel Classifiers | Theory and Algorithms | Ralf Herbrich | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2022 | MIT Press | EAN 9780262546591 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. 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.
