PCP (Pattern Classification Program) is a machine learning
program for supervised classification of
patterns. It runs in interactive and batch modes, and
implements the following machine learning algorithms and
methods: k-means clustering, Fisher's linear discriminant,
dimension reduction using Singular Value Decomposition,
Principal Component Analysis, feature subset selection,
Bayes error estimation, parametric classifiers (linear and
quadratic), pseudo-inverse linear discriminant, k-Nearest
Neighbor method, neural networks, Support Vector
Machine algorithm (SVM), model selection for SVM, cross-validation, and bagging
(committee)
classification.
