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m contents.m
% ICALAB for Signal Processing
% (abbreviation of Independent Component Analysis Laboratory)
%
% developed and tested under Matlab versions 5.3 and higher
% Version 2.2, February 24, 2004
% Autho
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% General purpose and others functions for STPRToolbox.
%
% cerror - Calculates classifier error.
% cliplin1 - Clips line according to given window.
% cliplin2 - Clips line starting i
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% Bayes Classification.
%
% bayeserr - Computes the Bayesian risk for optimal classifier.
% bayescln - Classifier based on Bayes decision rule for Gaussians.
% bayesnd - Discrim. function, dic
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% Statistical Pattern Recognition Toolbox.
%
% Contents
%
% bayes - (dir) Bayes classification.
% datasets - (dir) Functions for handling with data sets.
% generalp - (dir) General purpose
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% Minimax learning algorithm.
%
% mmdemo - Demonstration of the minimax learning algorithm.
% mmln - Minimax learning algorithm for estimation of
% normal distribut
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% Unsupervised statistical learning methods.
%
% unsudemo - Demo of unsupervised learning methods for 2D feature space.
%
% mln - Compute value of logarihm of the likelihood function.
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% Statistical learning methods.
%
% Included directories (implementing algorithms):
% minimax - (dir) Minimax learning algorithm.
% unsuper - (dir) Unsupervised learning methods, EM algori
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% Principal Component Analysis.
%
% kernelpca - Non-linear version of PCA.
% pkenrelpca - Vizualizes Kernel-PCA mapping in 2D.
% spca - Standard linear PCA (Karhunen-Loeve).
%
%
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% Quadratic discriminant function.
%
% quaddemo - Demonstrates use of non-linear data mapping.
%
% qtransf - Non-linear mapping for quadratic discriminant function.
% quad2d - Computes
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% Support Vector Machines.
%
% msmo - Multi-class version of SMO.
% msvmclass - Multi-class version of SVMCLASS.
% msvmmot - Multi-class version of SVMMOT.
% ka - Kernel-Adatron algo