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PCP (Pattern Classification Program) is an machine learning program for supervised and unsupervised 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
- 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
- cross-validation
- bagging (committee) classification


Download version 1.2 (beta)
released on 27 February 2005



LicenseVerified byVerified onNotes
X11Janet Casey16 March 2005

Leaders and contributors

Ljubomir Buturovic Maintainer

Resources and communication

Audience Resource type URI
Bug Tracking,Developer,Support E-mail

Software prerequisites

This entry (in part or in whole) was last reviewed on 16 March 2005.


Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the page “GNU Free Documentation License”.

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