Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning
Author :
Publisher : MIT Press
Total Pages : 266
Release :
ISBN-10 : 9780262182539
ISBN-13 : 026218253X
Rating : 4/5 (53X Downloads)

Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen

Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Gaussian Processes for Machine Learning Related Books

Gaussian Processes for Machine Learning
Language: en
Pages: 266
Authors: Carl Edward Rasmussen
Categories: Computers
Type: BOOK - Published: 2005-11-23 - Publisher: MIT Press

GET EBOOK

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machi
Lectures on Gaussian Processes
Language: en
Pages: 129
Authors: Mikhail Lifshits
Categories: Mathematics
Type: BOOK - Published: 2012-01-11 - Publisher: Springer Science & Business Media

GET EBOOK

Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables. Their theory presents a powerful range o
Gaussian Processes, Function Theory, and the Inverse Spectral Problem
Language: en
Pages: 354
Authors: Harry Dym
Categories: Mathematics
Type: BOOK - Published: 2008-01-01 - Publisher: Courier Corporation

GET EBOOK

This text offers background in function theory, Hardy functions, and probability as preparation for surveys of Gaussian processes, strings and spectral function
Modelling and Control of Dynamic Systems Using Gaussian Process Models
Language: en
Pages: 281
Authors: Juš Kocijan
Categories: Technology & Engineering
Type: BOOK - Published: 2015-11-21 - Publisher: Springer

GET EBOOK

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of sy
Markov Processes, Gaussian Processes, and Local Times
Language: en
Pages: 4
Authors: Michael B. Marcus
Categories: Mathematics
Type: BOOK - Published: 2006-07-24 - Publisher: Cambridge University Press

GET EBOOK

This book was first published in 2006. Written by two of the foremost researchers in the field, this book studies the local times of Markov processes by employi