This is a script file.


 See also

   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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