Machine learning program for pattern classification
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 25 May 2006
16 March 2005
Leaders and contributors
Resources and communication
This entry (in part or in whole) was last reviewed on 2 March 2017.
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