PCP Application 2.2

This application 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 The program supports interactive and batch processing. Commands are issued through a keyboard-driven menu system in the interactive mode, or in a batch file in the batch mode. It is a binary executable and does not need any special run-time environment. PCP uses tab-delimited text files for input data. The results are displayed on the screen and saved in text files.

Requirements:	No special requirements
Platforms:	Windows, Mac, *nix, Mac OSX, Linux, C/C++ , BSD Solaris
Keyword:	Classification Of Patterns,  Learning,  Machine,  Machine Learning Program,  Patterns Classification Algorithm,  Program
Users rating:	0/10