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
- bagging (committee) classification
released on 27 February 2005
|License||Verified by||Verified on||Notes|
|X11||Janet Casey||16 March 2005|
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This entry (in part or in whole) was last reviewed on 16 March 2005.
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