Pattern recognition and machine learning
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models.
xx, 738 pages : illustrations (chiefly color) ; 24 cm
9780387310732, 0387310738
228415823
Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
(Corrected printing 2007)--Title page verso