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

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

Classes in gov.sandia.cognition.learning.algorithm.tree used by gov.sandia.cognition.learning.algorithm.tree
AbstractDecisionTreeLearner
          The AbstractDecisionTreeLearner implements common functionality for learning algorithms that learn a decision tree.
AbstractDecisionTreeNode
          The AbstractDecisionTreeNode class implements common functionality for a decision tree node.
AbstractVectorThresholdMaximumGainLearner
          An abstract class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
CategorizationTree
          The CategorizationTree class extends the DecisionTree class to implement a decision tree that does categorization.
CategorizationTreeNode
          The CategorizationTreeNode implements a DecisionTreeNode for a tree that does categorization.
DeciderLearner
          The DeciderLearner interface defines the functionality of a learner that can be used to learn a decision function inside a decision tree.
DecisionTree
          The DecisionTree class implements a standard decision tree that is made up of DecisionTreeNode objects.
DecisionTreeNode
          The DecisionTreeNode interface defines the functionality of a node in a decision tree.
PriorWeightedNodeLearner
          The PriorWeightedNodeLearner interface specifies the ability to configure prior weights on the learning algorithm that searches for a decision function inside a decision tree.
RegressionTree
          The RegressionTree class extends the DecisionTree class to implement a decision tree that does regression.
RegressionTreeNode
          The RegressionTreeNode implements a DecisionTreeNode for a tree that does regression.
VectorThresholdMaximumGainLearner
          An interface class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.