wip/pcp 2.2 NetBSD

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