Pattern Classification Program is an open-source machine learning program for supervised and unsupervised classification of patterns (vectors of measurements). Pattern Classification Program implements the following 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)
· least-squares (pseudo-inverse) linear discriminant
· k-Nearest Neighbor
· neural networks (Multi-Layer Perceptron)
· Support Vector Machine algorithm
· 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.
PCP runs under Linux and Windows operating systems (under Cygwin environment), on i386 architecture CPUs such as Intel Pentium or AMD Athlon. PCP has been developed and tested on RedHat Linux 9.0 distribution. It has also been tested on SUSE Linux 9.1 and Fedora Core 2 and verified to run on Knoppix 3.7 and Windows XP.
What's New in This Release:
· 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.