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


Information from electronic data provided by the publisher. May be incomplete or contain other coding.


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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.