Projects / Pattern Classification Program

Pattern Classification Program

PCP (Pattern Classification Program) is a machine learning program for supervised 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, dimension reduction using 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 (SVM), model selection for SVM, cross-validation, and bagging (committee) classification.

Operating Systems

Recent releases

  •  25 May 2006 09:09

    Release Notes: This release supports model selection for the linear SVM kernel and an option to build SVD transforms using training and test datasets (as opposed to just training data). P-errors are now reported in SVM model selection. The build process was simplified.

    •  04 Feb 2006 02:20

      Release Notes: This release creates the prediction file pcp.rcl for MLP prediction, implements MLP model selection, implements k-NN model selection, has additional information in the class prediction file pcp.rcl (correct classification flag, TP, FN, FP, and TN flags for two-class cases), removes a major memory handling defect in the forward selection algorithm that lead to poor (computational) performance, enforces the feasible region for nu in NU-SVM, and changes the default number of cross-validation experiments from 10 to 1.

      •  01 Jul 2005 04:49

        Release Notes: LIBSVM was upgraded to version 2.71. Pearson correlation was added as a feature selection criterion. Clustering was removed. The distance selection menu was removed. Individual class costs are supported for C-SVM learning. FORTRAN code was eliminated. A Model Selection menu for the Support Vector Machine algorithm was added. Forward selection and backward elimination feature subset selection algorithms were added. Inter-intra distance, 1-NN error rate, and Bayes error rate were added as criteria for feature selection. GNU autoconf is now used to build PCP.

        •  28 Feb 2005 03:55

          Release Notes: Ported to Windows (under the Cygwin environment). Minor bugfixes and updated documentation.

          •  17 Feb 2005 22:28

            Release Notes: The maximum number of attributes for clustering has been increased to 1000. The training data set is used (as opposed to test data set) in clustering. A verbose mode for the clustering summary has been implemented. A formatting bug in saving the results of clustering has been fixed. 'named rows (vectors)' and 'named columns (attributes)' input data file formats are supported. The Golub (ALL/AML) data set is now provided in the more useful named rows/named columns format.


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