Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

作者: 
Christopher M. Bishop
语言: 
ISBN: 
9780387310732
出版日期: 
星期五, 四月 1, 2011

简介

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

目录

Preface
Mathematical notation
Contents
1 Introduction
2 Probability Distributions
3 Linear Models for Regression
4 Linear Models for Classification
5 Neural Networks
6 Kernel Methods
7 Sparse Kernel Machines
8 Graphical Models
9 Mixture Models and EM
10 Approximate Inference
11 Sampling Methods
12 Continuous Latent Variables
13 Sequential Data
14 Combining Models
Appendix A Data Sets
Appendix B Probability Distributions
Appendix C Properties of Matrices
Appendix D Calculus of Variations
Appendix E Lagrange Multipliers
References
Index