Pattern recognition presents a significant challege for scientists and engineers, and many different approaches have been proposed. This book provides a self-contained account of probabilistic techniques that have been applied to the subject. Researchers and graduate students will benefit from this wide-ranging account of the field.

Inhalt
Preface * Introduction * The Bayes Error * Inequalities and alternate
distance measures * Linear discrimination * Nearest neighbor rules *
Consistency * Slow rates of convergence Error estimation * The regular
histogram rule * Kernel rules Consistency of the k-nearest neighbor
rule * Vapnik-Chervonenkis theory * Combinatorial aspects of Vapnik-
Chervonenkis theory * Lower bounds for empirical classifier selection

• The maximum likelihood principle * Parametric classification *
  Generalized linear discrimination * Complexity regularization *
  Condensed and edited nearest neighbor rules * Tree classifiers * Data-
  dependent partitioning * Splitting the data * The resubstitution
  estimate * Deleted estimates of the error probability * Automatic
  kernel rules * Automatic nearest neighbor rules * Hypercubes and
  discrete spaces * Epsilon entropy and totally bounded sets * Uniform
  laws of large numbers * Neural networks * Other error estimates *
  Feature extraction * Appendix * Notation * References * Index