Package edu.udo.cs.yale.operator.learner

Provides learning operators.

See:
Description

Interface Summary

Distribution	Distribution is an interface for a distribution class.
Learner	A Learner is an operator that encapsulates the learning step of a machine learning method.

Class Summary

AbstractLearner	A Learner is an operator that encapsulates the learning step of a machine learning method.
BestRuleInduction	This operator returns the best rule regarding WRAcc using exhaustive search.
ConjunctiveRuleModel	Each object of this class represents a conjunctive rule with boolean target and nominal attributes.
DiscreteDistribution	DiscreteDistribution is an distribution for nominal values.
DistributionModel	DistributionModel is a model for learner, which generate estimated distributions for prediction.
LearnerCapability	The possible capabilities for all learners.
MultiCriterionDecisionStumps	A DecisionStump clone that allows to specify different utility functions.
NaiveBayes	NaiveBayes is a lerner, using normal distributions to estimate real distribution of data
NormalDistribution	Normaldistribution is a distribution, calculating the probaility for a given value from an gaussian normal distribution.
PredictionModel	PredictionModel is the superclass for all objects generated by learners, i.e.
SimpleBinaryPredictionModel	A model that can be applied to an example set by applying it to each example separately.
SimplePredictionModel	A model that can be applied to an example set by applying it to each example separately.

Package edu.udo.cs.yale.operator.learner Description

Provides learning operators. These receive an example set as input and return a model for predicting the labels of new and unlabelled examples.