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Hardcover. Condizione: 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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Condizione: 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.

Condizione: New. pp. xviii + 248.

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Hardcover. Condizione: New. Brand new. Excellent customer service.

Condizione: As New. Unread book in perfect condition.

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Paperback or Softback. Condizione: New. Pattern Recognition: 26th Dagm Symposium, August 30 - September 1, 2004, Proceedings. Book.

Hardcover. Condizione: new. Hardcover. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardback. Condizione: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Hardback. Condizione: New.

HARDCOVER. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Hardback. Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. pp. xviii + 248 Illus.

Condizione: New. 2006. Hardcover. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Series: Adaptive Computation and Machine Learning Series. Num Pages: 266 pages, Illustrations. BIC Classification: PBW; UYQM. Category: (P) Professional & Vocational. Dimension: 261 x 212 x 18. Weight in Grams: 720. . . . . .…

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: New.

Paperback. Condizione: New. 2004 ed. We are delighted to present the proceedings of DAGM 2004, and wish to - press our gratitude to the many people whose e?orts made the success of the conference possible. We received 146 contributions of which we were able to - cept 22 as oral presentations and 48 as posters. Each paper received 3 reviews, upon which decisions were based. We are grateful for the dedicated work of the 38 members of the program committee and the numerous referees. The careful review process led to the exciting program which we are able to present in this volume. Among the highlights of the meeting were the talks of our four invited spe- ers, renowned experts in areas spanning learning in theory, in vision and in robotics: - William T. Freeman, Arti?cial Intelligence Laboratory, MIT: Sharing F- tures for Multi-class Object Detection - PietroPerona,Caltech:TowardsUnsupervisedLearningofObjectCategories - StefanSchaal,DepartmentofComputerScience,UniversityofSouthernC- ifornia: Real-Time Statistical Learning for Humanoid Robotics - Vladimir Vapnik, NEC Research Institute: Empirical Inference WearegratefulforeconomicsupportfromHondaResearchInstituteEurope, ABW GmbH, Transtec AG, DaimlerChrysler, and Stemmer Imaging GmbH, which enabled us to ?nance best paper prizes and a limited number of travel grants. Many thanks to our local support Sabrina Nielebock and Dagmar Maier, who dealt with the unimaginably diverse range of practical tasks involved in planning a DAGM symposium. Thanks to Richard van de Stadt for providing excellent software and support for handling the reviewing process. A special thanks goes to Jeremy Hill, who wrote and maintained the conference website.…

Hardcover. Condizione: Brand New. 2448 pages. 10.00x7.00x1.00 inches. In Stock.

Condizione: New. pp. xviii + 248.

Hardcover, 248 pages. Condizione: Good. Ninth Printing. Ninth Printing. No DJ. Gilding on outer spine. Scuffing and minor soiling to boards. Bumping to outer spine and boards. Light soiling to text block. Pages are clean and appear to be free of writing and highlighting. Binding is tight.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Condizione: New. In English.

Condizione: New. 2006. Hardcover. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Series: Adaptive Computation and Machine Learning Series. Num Pages: 266 pages, Illustrations. BIC Classification: PBW; UYQM. Category: (P) Professional & Vocational. Dimension: 261 x 212 x 18. Weight in Grams: 720. . . . . . Books ship from the US and Ireland.…