PCP (Pattern Classification Program) is an open-source
machine learning program for supervised classification
of patterns (vectors of measurements).
PCP implements the following algorithms and methods:
* Fisher's linear discriminant
* dimensionality reduction using Singular Value Decomposition
* Principal Component Analysis
* feature subset selection
* Bayes error estimation
* parametric classifiers (linear and quadratic)
* least-squares (pseudo-inverse) linear discriminant
* k-Nearest Neighbor (k-NN)
* neural networks (Multi-Layer Perceptron (MLP))
* Support Vector Machine (SVM) algorithm
* SVM, MLP and k-NN model selection
* cross-validation
* bagging (committee) classification
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