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See:
Description
Interface Summary  

Discretizer 
Class Summary  

Cluster  Implements a cluster center that has a mean vector and a covariance matrix (and its inverse) 
ClusteredDataGenerator  Generates clustered data for testing machine learning algorithms 
ContextualGMMParams  Wrapper for contextual parameters for GMM training  includes various phone identity or class based groups 
GaussianComponent  Implements a single Gaussian component with a mean vector and a covariance matrix It also computes terms for pdf computation out of this Gaussian component once the mean and covariance is specified 
GMM  Wrapper for a Gaussian Mixture Model 
GMMClassifier  TO DO: Implement a GMM based classifier that takes as input several GMMs and data and outputs the probability of each GMM generating the data, the most likely GMM, etc 
GmmDiscretizer  This discretizes values according to a gaussian mixture model (gmm). 
GMMTrainer  ExpectationMaximization (EM) based GMM training Reference: A. 
GMMTrainerParams  Wrapper class for GMM training parameters 
KMeansClusteringTrainer  KMeans clustering training algorithm Reference: J. 
KMeansClusteringTrainerParams  Wrapper class for KMeans clustering training parameters 
PolynomialCluster  Implements a cluster center that has a mean 
PolynomialHierarchicalClusteringTrainer  Hierarchical clustering training algorithm Reference: Stephen C. 
PolynomialKMeansClusteringTrainer  KMeans clustering training algorithm Reference: J. 
SFFS  Sequential Floating Forward Search(SFFS) for selection of features Ref: Pudil, P., J. 
SoP  Contains the coefficients and factors of an equation of the form: if interceptTterm = TRUE solution = coeffs[0] + coeffs[1]*factors[0] + coeffs[2]*factors[1] + ... 
Machine learning classes for KMeans clustering, Gaussian Mixture
Models, and manual data generation for testing purposes.


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