Implemented methods: Statistical Pattern Recognition Toolbox

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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