Pattern recognition and machine learning
Author(s)
Bibliographic Information
Pattern recognition and machine learning
（Information science and statistics / series editors M. Jordan ... [et al.]）
Springer, c2006
- : pbk
Related Bibliography 1 items
Available at / 213 libraries
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University Library for Agricultural and Life Sciences, The University of Tokyo図
: hard007.13:B475011233193
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Note
Includes bibliographical references (p. 711-728) and index
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"
