Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams.

Format:

Book

Author/Creator:

Rasmussen, Carl Edward.

Contributor:

Williams, Christopher K. I.

Series:

Adaptive computation and machine learning.

Adaptive computation and machine learning

Language:

English

Subjects (All):

Gaussian processes--Data processing.

Gaussian processes.

Machine learning--Mathematical models.

Machine learning.

Physical Description:

xviii, 248 p. : ill.

Edition:

1st ed.

Place of Publication:

Cambridge, Mass. : MIT Press, c2006.

Summary:

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Contents:

Intro

Series Foreword

Preface

Symbols and Notation

Chapter 1 Introduction

Chapter 2 Regression

Chapter 3 Classification

Chapter 4 Covariance functions

Chapter 5 Model Selection and Adaptation of Hyperparameters

Chapter 6 Relationships between GPs and Other Models

Chapter 7 Theoretical Perspectives

Chapter 8 Approximation Methods for Large Datasets

Chapter 9 Further Issues and Conclusions

Appendix A Mathematical Background

Appendix B Gaussian Markov Processes

Appendix C Datasets and Code

Bibliography

Author Index

Subject Index.

Notes:

Title from title screen.

Includes bibliographical references (p. [223]-238) and indexes.

Digitized and made available by: Books24x7.com.

ISBN:

0-262-26107-3

9786612097966

1-4237-6990-2

OCLC:

68194203