Table of contents for Pattern recognition and machine learning / Christopher M. Bishop.

Bibliographic record and links to related information available from the Library of Congress catalog

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Introduction.- 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.

Library of Congress subject headings for this publication:
Pattern perception.
Machine learning.