Książka Gaussian Processes for Machine Learning Rasmussen

Gaussian Processes for Machine Learning

Autor: Rasmussen
Język: Angielski
Oprawa: Twarda
Wydawca: MIT Press Ltd
Dostępność: Dostępna u dostawcy w małych ilościach
Wysyłamy za 11-15 dni
260.08
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kern...

Informacje o książce

Autor
Język
Angielski
Oprawa
Książka - Twarda
Data wydania
2005
strony
272
EAN
9780262182539
ISBN
026218253X
Enbook ID
04397459
Wydawca
Waga
826
Wymiary
263 x 212 x 16

Pełny opis

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.

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