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Gaussian Processes for Machine Learning (Hardcover)

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Product Overview

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.

Specifications

Publisher Mit Pr
Mfg Part# 9780262182539
SKU 202122708
Format Hardcover
ISBN10 026218253X
Release Date 4/10/2007
Product Attributes
Book Format Hardcover
Minimum Age 22
Number of Pages 0266
Publisher MIT Press (MA)
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