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

• Superior document: Adaptive computation and machine learning
• Author: Rasmussen, Carl Edward.
• TeilnehmendeR: Williams, Christopher K. I.
• Year of Publication: 2006
• Edition: 1st ed.
• Language: English
• Series: Adaptive computation and machine learning.
• Physical Description: xviii, 248 p. :; ill.
• Notes: Title from title screen.

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

Table of 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.