Learning kernel classifiers theory de herbrich ralf (23 resultados)

Hardcover. Condición: Very Good. With dust jacket. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Condición: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

paperback. Condición: 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.

Hardcover. Condición: Very Good. Estado de la sobrecubierta: 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 ONLY POST TO AUSTRALIA NZ CANADA USA JAPAN SINGAPORE SWITZERLAND & UK VIA THIS SERVICE*** .…

Condición: As New. Unread book in perfect condition.

Condición: New.

hardcover. Condición: 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.…

Condición: New.

Condición: New. In English.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: Brand New. 384 pages. 9.00x7.00x0.89 inches. In Stock.

Hardcover. Condición: new. New Copy. Customer Service Guaranteed.

Couverture rigide. Condición: Neuf.

paperback. Condición: New. New .Ships From Multiple Locations. book.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Taschenbuch. Condición: 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.…

Taschenbuch. Condición: 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.…