mplemented methods: Statistical Pattern Recognition Toolbox
The following list is a digest of the methods implemented in the toolbox:Linear discriminant function
Perceptron and multi-class modification
Epsilon-optimal separating hyperplane by Kozinec's algorithm
Fisher Linear Discriminant
Algorithms to solve the Generalized Anderson's task
Feature extraction
Principal Component Analysis
Kernel PCA
Greedy Kernel PCA
Linear Discriminant Analysis
Generalized Discriminant Analysis
Density estimation and clustering
Gaussian mixture models
Expectation-Maximization algorithm for Gaussian mixture models
Minimax estimation for Gaussian distribution
Reduced Set Density Estimator
Fitting sigmoid to classifier output
K-means clustering
Support Vector Machines
Sequential Minimal Optimizer
SVM based on Matlab Optimization toolbox
Matlab interface to SVM^{light}
Solvers for multi-class BSVM formulation.
Single-class SVM solvers
Kernel Fisher Discriminant
Kernel Perceptron
Others
Bayes classifier, error estimation
Cross-validation evaluation
k-Nearest Neighbor rule
Quadratic data mapping
Visualization
Discriminant functions
Probabilistic models
SVM classifiers
Images
Regression
Interface to XTAL regression package which implements:Projection pursuit regression (SMART)
Multilayer perceptron
Multivariate adaptive regression splines
k-nearest neighbors and Constrained topological mapping
Constrained topological mapping
