Uses of Interface
gov.sandia.cognition.learning.algorithm.tree.DeciderLearner

Packages that use DeciderLearner
gov.sandia.cognition.learning.algorithm.tree Provides decision tree learning algorithms. 
 

Uses of DeciderLearner in gov.sandia.cognition.learning.algorithm.tree
 

Subinterfaces of DeciderLearner in gov.sandia.cognition.learning.algorithm.tree
 interface VectorThresholdMaximumGainLearner<OutputType>
          An interface class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
 

Classes in gov.sandia.cognition.learning.algorithm.tree that implement DeciderLearner
 class AbstractVectorThresholdMaximumGainLearner<OutputType>
          An abstract class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
 class RandomSubVectorThresholdLearner<OutputType>
          Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner.
 class VectorThresholdGiniImpurityLearner<OutputType>
          Learns vector thresholds based on the Gini impurity measure.
 class VectorThresholdHellingerDistanceLearner<OutputType>
          A categorization tree decision function learner on vector data that learns a vector value threshold function using the Hellinger distance.
 class VectorThresholdInformationGainLearner<OutputType>
          The VectorThresholdInformationGainLearner computes the best threshold over a dataset of vectors using information gain to determine the optimal index and threshold.
 class VectorThresholdVarianceLearner
          The VectorThresholdVarianceLearner computes the best threshold over a dataset of vectors using the reduction in variance to determine the optimal index and threshold.
 

Fields in gov.sandia.cognition.learning.algorithm.tree declared as DeciderLearner
protected  DeciderLearner<? super InputType,OutputType,?,?> AbstractDecisionTreeLearner.deciderLearner
          The learning algorithm for the decision function.
protected  DeciderLearner<Vectorizable,OutputType,Boolean,VectorElementThresholdCategorizer> RandomSubVectorThresholdLearner.subLearner
          The decider learner for the subspace.
 

Methods in gov.sandia.cognition.learning.algorithm.tree that return DeciderLearner
 DeciderLearner<? super InputType,OutputType,?,?> AbstractDecisionTreeLearner.getDeciderLearner()
          Gets the learner for the decision function.
 DeciderLearner<Vectorizable,OutputType,Boolean,VectorElementThresholdCategorizer> RandomSubVectorThresholdLearner.getSubLearner()
          Gets the learner used to learn a threshold function over the subspace.
 

Methods in gov.sandia.cognition.learning.algorithm.tree with parameters of type DeciderLearner
 void AbstractDecisionTreeLearner.setDeciderLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner)
          Sets the learner for the decision function.
 void RandomSubVectorThresholdLearner.setSubLearner(DeciderLearner<Vectorizable,OutputType,Boolean,VectorElementThresholdCategorizer> subLearner)
          Sets the learner used to learn a threshold function over the subspace.
 

Constructors in gov.sandia.cognition.learning.algorithm.tree with parameters of type DeciderLearner
AbstractDecisionTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner)
          Creates a new instance of AbstractDecisionTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner)
          Creates a new instance of CategorizationTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner, int leafCountThreshold, int maxDepth)
          Creates a new instance of CategorizationTreeLearner.
CategorizationTreeLearner(DeciderLearner<? super InputType,OutputType,?,?> deciderLearner, int leafCountThreshold, int maxDepth, Map<OutputType,Double> priors)
          Creates a new instance of CategorizationTreeLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,Boolean,VectorElementThresholdCategorizer> subLearner, double percentToSample, Random random)
          Creates a new RandomSubVectorThresholdLearner.
RandomSubVectorThresholdLearner(DeciderLearner<Vectorizable,OutputType,Boolean,VectorElementThresholdCategorizer> subLearner, double percentToSample, Random random, VectorFactory<? extends Vector> vectorFactory)
          Creates a new RandomSubVectorThresholdLearner.
RegressionTreeLearner(DeciderLearner<? super InputType,Double,?,?> deciderLearner, BatchLearner<Collection<? extends InputOutputPair<? extends InputType,Double>>,? extends Evaluator<? super InputType,Double>> regressionLearner)
          Creates a new instance of CategorizationTreeLearner.
RegressionTreeLearner(DeciderLearner<? super InputType,Double,?,?> deciderLearner, BatchLearner<Collection<? extends InputOutputPair<? extends InputType,Double>>,? extends Evaluator<? super InputType,Double>> regressionLearner, int leafCountThreshold, int maxDepth)
          Creates a new instance of CategorizationTreeLearner.