CLASSIFY See also Clustering: demoCluster - Clustering demo. demoGenData - Generate data drawn form a mixture of Gaussians. kmeans2 - Fast version of kmeans clustering. meanShift - meanShift clustering algorithm. meanShiftIm - Applies the meanShift algorithm to a joint spatial/range image. meanShiftImExplore - Visualization to help choose sigmas for meanShiftIm. Calculating distances efficiently: distMatrixShow - Useful visualization of a distance matrix of clustered points. pdist2 - Calculates the distance between sets of vectors. softMin - Calculates the softMin of a vector. Principal components analysis: pca - Principal components analysis (alternative to princomp). pcaApply - Companion function to pca. pcaRandVec - Generate random vectors in PCA subspace. pcaVisualize - Visualization of quality of approximation of X given principal comp. visualizeData - Project high dim. data unto principal components (PCA) for visualization. Confusion matrix display: confMatrix - Generates a confusion matrix according to true and predicted data labels. confMatrixShow - Used to display a confusion matrix. Radial Basis Functions (RBFs): rbfComputeBasis - Get locations and sizes of radial basis functions for use in rbf network. rbfComputeFtrs - Evaluate features of X given a set of radial basis functions. rbfDemo - Demonstration of rbf networks for regression. Fast random fern/forest classification/regression code: fernsClfApply - Apply learned fern classifier. fernsClfTrain - Train random fern classifier. fernsInds - Compute indices for each input by each fern. fernsRegApply - Apply learned fern regressor. fernsRegTrain - Train boosted fern regressor. forestApply - Apply learned forest classifier. forestTrain - Train random forest classifier. Fast boosted decision tree code: adaBoostTrain - Train boosted decision tree classifier. adaBoostApply - Apply learned boosted decision tree classifier. binaryTreeTrain - Train binary decision tree classifier. binaryTreeApply - Apply learned binary decision tree classifier.

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