% Statistical Pattern Recognition Toolbox (STPRtool).% Version 2.04       22-Dec-2004%% Bayesian classification.%   bayescls         - Bayesian classifier with reject option.%   bayesdf          - Computes decision boundary of Bayesian classifier.%   bayeserr         - Computes Bayesian risk for 1D case with Gaussians.%% Linear Discriminant function.%   linclass         - Linear classifier.%   ekozinec         - Kozinec's algorithm for eps-optimal hyperplane.%   mperceptron      - Perceptron to train multi-class linear classifier.%   perceptron       - Perceptron to train binary linear classifier.%   fld              - Fisher Linear Discriminant. %   fldqp            - Fisher Linear Discriminant using QP.%% Generalized Anderson's task.%   andrerr          - Classification error of the Generalized Anderson's task.%   androrig         - Original method to solve the Anderson's task.%   eanders          - Epsilon-solution of the Generalized Anderson's task.%   ganders          - Solves the Generalized Anderson's task.%   ggradander       - Generalized gradients approach to Gen. Anderson's task.% % Linear feature extraction.%   linproj          - Linear data projection.%   lda              - Linear Discriminant Analysis.%   pca              - Principal Component Analysis.%   pcarec           - Computes reconstructed vector after PCA projection.%% Miscellaneous methods.%   adaboost         - AdaBoost algorithm.%   adaclass         - AdaBoost classifier.%   cerror           - Computes classification error.%   crossval         - Partions data for cross-validation.%   knnclass         - k-Nearest Neighbours classifier.%   knnrule          - Creates K-nearest neighbours classifier.%   roc              - Computes Receiver Operator Characteristic.%   weaklearner      - Produces classifier thresholding single feature.%% Kernel machines.%   diagker         - Returns diagonal of kernel matrix.%   dualcov         - Dual representation of covariance matrix.%   dualmean        - Computes dual representation of mean vector.%   kdist           - Computes distance between points in kernel space. %   kernel          - Evaluates kernel function.%   kernelproj      - Kernel projection.    %   kfd             - Kernel Fisher Discriminant.%   knorm           - Computes L2-norm in kernel space.%   kperceptr       - Kernel Perceptron.%   lin2svm         - Merges linear rule and kernel projection. %   minball         - Minimal enclosing ball in kernel feature space.%   rsrbf           - Reduced Set Method for RBF kernel expansion.   %   rspoly2         - Reduced Set Method for homegeneous 2nd polynomial kernel.% % Kernel feature extraction.%   gda             - Generalized Discriminant Analysis.%   greedykpca      - Greedy kernel PCA.%   kpca            - Kernel Principal Component Analysis.%   kpcarec         - Reconstructs image after kernel PCA.%  % Optimization methods.%   gmnp            - Generalized Minimal Norm Problem (GMNP).%   gnnls           - Generalized Non-negative Least Squares Problem (GNNLS). %   gridsearch      - Function minimization using grid search.%% Pre-image problem for RBF kernel.%   rbfpreimg       - Schoelkopf's fixed-point algorithm.%   rbfpreimg2      - Gradient optimization.%   rbfpreimg3      - Kwok-Tsang's algorithm.%% Support Vector Machines.%   bsvm2           - Solver for multi-class BSVM with L2-soft margin.%   evalsvm         - Training and evaluates SVM classifier.%   mvsvmclass      - Majority voting multi-class SVM classifier.%   oaasvm          - Multi-class SVM using One-Agains-All decomposition.%   oaosvm          - Multi-class SVM using One-Against-One decomposition.%   smo             - Sequential Minimal Optimization for SVM (L1).%   svm1d           - Linear SVM for 1-dimensional input data.%   svm2            - Solver for binary SVM with L2 soft margin.%   svmclass        - Support Vector Machines Classifier. %   svmlight        - Interface to SVM^{light} software. %   svmquadprog     - SVM trained by Matlab Optimization Toolbox.%% Probability distribution functions and estimation.%   dsamp           - Generates samples from discrete distribution.%   erfc2           - Normal cumulative distribution function. %   gmmsamp         - Generates sample from Gaussian mixture model (GMM).%   gsamp           - Generates sample from Gaussian distribution.%   kmeans          - K-means clustering algorithm. %   mahalan         - Computes Mahalanobis distance.%   pdfgauss        - Computes probability for Gaussian distribution.%   pdfgmm          - Computes probability for Gaussian mixture model.  %   sigmoid         - Evaluates sigmoid function.%%   emgmm           - Expectation-Maximization Algorithm for GMM. %   mlcgmm          - ML estimation of GMM from complete data.%   mlsigmoid       - Fitting a sigmoid function using ML estimation.%   mmgauss         - Minimax estimation of Gaussian distribution.%   rsde            - Reduced Set Density Estimator.%% Quadratic discriminant function.%   lin2quad        - Merges linear rule and quadratic mapping.%   qmap            - Quadratic data mapping.%   quadclass       - Quadratic classifier.  %% Visualization.%   pandr           - Visualizes solution of the Generalized Anderson's task. %   pboundary       - Plots decision boundary of given classifier in 2D.%   pgauss          - Visualizes set of bivariate Gaussians.%   pgmm            - Visualizes Gaussian mixture model.%   pkernelproj     - Plots isolines of kernel projection.%   plane3          - Plots plane in 3d.%   pline           - Plots line in 2D.%   ppatterns       - Plots pattern as points in feature space.%   psvm            - Plots decision boundary of binary SVM classifier. %   showim          - Displays given image(s).  %   % Data sets.%   andersons_task   - (dir) Input for demo on Generalized Anderson's task.%   binary_separable - (dir) Input for demo on Linear classification.%   gmm_sample       - (dir) Input for demo on EM algorithm for GMM.%   iris_data        - (dir) Fisher's Iris data set.%   mm_sample        - (dir) Input for demo on Minimax Algorithm.%   multi_separable  - (dir) Linearly separable multi-class data.%   ocr_numerals     - (dir) Examples of hand-written numerals.%   riply_data       - (dir) Riply's data set.%   svm_sample       - (dir) Input for demo on SVM.%%   c2s              - Converts cell to structure array.%   createdata       - Interactive data generator. %   gencircledata    - Generates data on circle corrupted by Gaussian noise. %   genlsdata        - Generates linearly separable binary data.%   mergesets        - Merges data sets to one labeled data file.%   savestruct       - Saves fields of given structure to file.%   usps2mat         - Converts USPS database to Matlab data file (MAT). %% Demos.%   image_denoising - (dir) Image denoising using kernel PCA.%   ocr             - (dir) Optical Character Recognition.  %%   demo_anderson   - Generalized Anderson's task.%   demo_emgmm      - Expectation-Maximization algorithm for GMM.%   demo_kpcadenois - Idea of image denoising based on Kernel PCA.%   demo_linclass   - Algorithms learning linear classifiers.%   demo_mmgauss    - Minimax estimation of Gaussian distribution.%   demo_ocr        - Run OCR demo.%   demo_pcacomp    - Image compression using PCA.%   demo_svm        - Support Vector Machines.%   demo_svmpout    - Fitting a posteriori probability to SVM output.%%% compilemex - Compiles all MEX files of the STPRtool.% stprpath   - Sets path to the STPRtool.% % About: Statistical Pattern Recognition Toolbox% (C) 1999-2004, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 09-sep-2005, VF% 06-jun-2005, VF% 24-jan-2005, VF% 22-dec-2004, VF% 14-dec-2004, VF% 08-oct-2004, VF% 27-aug-2004, VF% 15-jun-2004, VF% 11-jun-2004, VF% 20-sep-2003, VF