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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../stpr.css"></head><body><h1 class="function"> Statistical Pattern Recognition Toolbox (STPRtool).</h1><pre> Version 2.05 19-Oct-2005 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 (GMNP) problem. gnnls - Generalized Non-negative Least Squares (GNNLS) problem. gnpp - Generalized Nearest Point (GNPP) problem. 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. <a href = "../root/compilemex.html" target="mdsbody">compilemex</a> - Compiles all MEX files of the STPRtool. <a href = "../root/stprpath.html" target="mdsbody">stprpath</a> - Sets path to the STPRtool. About: Statistical Pattern Recognition Toolbox (C) 1999-2005, 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</pre></body></html>⌨️ 快捷键说明
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