# Contents

## PURPOSE

CLASSIFY

## SYNOPSIS

This is a script file.

## DESCRIPTION

     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.