Condizione: 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.
Condizione: New. pp. xviii + 248.
EUR 48,07
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Condizione: New. Brand New. Excellent Customer Service.
EUR 50,93
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: INDOO, Avenel, NJ, U.S.A.
EUR 53,28
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread copy in mint condition.
Da: INDOO, Avenel, NJ, U.S.A.
EUR 53,37
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Brand New.
EUR 50,26
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. pp. xviii + 248 Illus.
Lingua: Inglese
Editore: MIT Press Ltd, Cambridge, Mass., 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
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.
EUR 53,04
Quantità: 1 disponibili
Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Condizione: NEW.
EUR 61,83
Quantità: 5 disponibili
Aggiungi al carrelloHardback. Condizione: New.
EUR 62,35
Quantità: Più di 20 disponibili
Aggiungi al carrelloHardback. Condizione: New.
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: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
EUR 56,29
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. pp. xviii + 248.
EUR 53,02
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: New.
Condizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Condizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 57,84
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: 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. . . . . .
Da: Revaluation Books, Exeter, Regno Unito
EUR 57,25
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 2448 pages. 10.00x7.00x1.00 inches. In Stock.
EUR 54,28
Quantità: 1 disponibili
Aggiungi al carrellohardcover. Condizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 59,63
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. In.
EUR 60,75
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 71,98
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: 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.
EUR 60,78
Quantità: Più di 20 disponibili
Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days.
EUR 46,91
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: NEW.
EUR 64,79
Quantità: Più di 20 disponibili
Aggiungi al carrelloHardback. Condizione: New.
Da: BennettBooksLtd, Los Angeles, CA, U.S.A.
hardcover. Condizione: New. In shrink wrap. Looks like an interesting title!
Lingua: Inglese
Editore: MIT Press Ltd, Cambridge, Mass., 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 98,35
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.