Uses of Package
gov.sandia.cognition.learning.algorithm.ensemble

Packages that use gov.sandia.cognition.learning.algorithm.ensemble
gov.sandia.cognition.learning.algorithm.ensemble Provides ensmble methods. 
gov.sandia.cognition.learning.algorithm.perceptron Provides the Perceptron algorithm and some of its variations. 
 

Classes in gov.sandia.cognition.learning.algorithm.ensemble used by gov.sandia.cognition.learning.algorithm.ensemble
AbstractBaggingLearner
          Learns an ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
AbstractUnweightedEnsemble
          An abstract implementation of the Ensemble interface for unweighted ensembles.
AbstractWeightedEnsemble
          An abstract implementation of the Ensemble interface for ensembles that have a weight associated with each member.
AveragingEnsemble
          An ensemble for regression functions that averages together the output value of each ensemble member to get the final output.
BaggingCategorizerLearner
          Learns an categorization ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
Ensemble
          The Ensemble interface defines the functionality of an "ensemble" that is typically created by combining together the result of multiple learning algorithms.
IVotingCategorizerLearner
          Learns an ensemble in a method similar to bagging except that on each iteration the bag is built from two parts, each sampled from elements from disjoint sets.
OnlineBaggingCategorizerLearner
          An implementation of an online version of the Bagging algorithm for learning an ensemble of categorizers.
VotingCategorizerEnsemble
          An ensemble of categorizers that determine the result based on an equal-weight vote.
WeightedBinaryEnsemble
          The WeightedBinaryEnsemble class implements an Ensemble of BinaryCategorizer objects where each categorizer is assigned a weight and the category is selected by choosing the one with the largest sum of weights.
WeightedVotingCategorizerEnsemble
          An ensemble of categorizers where each ensemble member is evaluated with the given input to find the category to which its weighted votes are assigned.
 

Classes in gov.sandia.cognition.learning.algorithm.ensemble used by gov.sandia.cognition.learning.algorithm.perceptron
WeightedBinaryEnsemble
          The WeightedBinaryEnsemble class implements an Ensemble of BinaryCategorizer objects where each categorizer is assigned a weight and the category is selected by choosing the one with the largest sum of weights.