Package cc.mallet.classify

Classes for training and classifying instances.

See: Description

Interface Summary

Interface	Description
Boostable	This interface is a tag indicating that the classifier attends to the InstanceList.getInstanceWeight() weights when training.
ClassifierTrainer.ByActiveLearning<C extends Classifier>	For active learning, in which this trainer will select certain instances and request that the Labeler instance label them.
ClassifierTrainer.ByIncrements<C extends Classifier>	For various kinds of online learning by batches, where training instances are presented, consumed for learning immediately.
ClassifierTrainer.ByInstanceIncrements<C extends Classifier>	For online learning that can operate on one instance at a time.
ClassifierTrainer.ByOptimization<C extends Classifier>

Class Summary

Class	Description
AdaBoost	AdaBoost Robert E.
AdaBoostM2	AdaBoostM2
AdaBoostM2Trainer	This version of AdaBoost can handle multi-class problems.
AdaBoostTrainer	This version of AdaBoost should be used only for binary classification.
BaggingClassifier
BaggingTrainer	Bagging Trainer.
BalancedWinnow	Classification methods of BalancedWinnow algorithm.
BalancedWinnowTrainer	An implementation of the training methods of a BalancedWinnow on-line classifier.
C45	A C4.5 Decision Tree classifier.
C45.Node
C45Trainer	A C4.5 decision tree learner, approximtely.
Classification	The result of classifying a single instance.
Classifier	Abstract parent of all Classifiers.
ClassifierAccuracyEvaluator
ClassifierEnsemble	Classifer for an ensemble of classifers, combined with learned weights.
ClassifierEnsembleTrainer
ClassifierEvaluator
ClassifierTrainer<C extends Classifier>	Each ClassifierTrainer trains one Classifier based on various interfaces for consuming training data.
ClassifierTrainer.Factory<CT extends ClassifierTrainer<? extends Classifier>>	Instances of a Factory know how to create new ClassifierTrainers to apply to new Classifiers.
ConfidencePredictingClassifier
ConfidencePredictingClassifierTrainer
DecisionTree	Decision Tree classifier.
DecisionTree.Node
DecisionTreeTrainer	A decision tree learner, roughly ID3, but only to a fixed given depth in all branches.
DecisionTreeTrainer.Factory
FeatureConstraintUtil	Utility functions for creating feature constraints that can be used with GE training.
FeatureSelectingClassifierTrainer	Adaptor for adding feature selection to a classifier trainer.
MaxEnt	Maximum Entropy (AKA Multivariate Logistic Regression) classifier.
MaxEntGERangeTrainer	Training of MaxEnt models with labeled features using Generalized Expectation Criteria.
MaxEntGETrainer	Training of MaxEnt models with labeled features using Generalized Expectation Criteria.
MaxEntL1Trainer
MaxEntOptimizableByGE
MaxEntOptimizableByLabelDistribution
MaxEntOptimizableByLabelLikelihood
MaxEntPRTrainer	Penalty (soft) version of Posterior Regularization (PR) for training MaxEnt.
MaxEntTrainer	The trainer for a Maximum Entropy classifier.
MCMaxEnt	Maximum Entropy classifier.
MCMaxEntTrainer	The trainer for a Maximum Entropy classifier.
MostFrequentClassAssignmentTrainer	A Classifier Trainer to be used with MostFrequentClassifier.
MostFrequentClassifier	A Classifier that will return the most frequent class label based on a training set.
NaiveBayes	A classifier that classifies instances according to the NaiveBayes method.
NaiveBayesEMTrainer
NaiveBayesTrainer	Class used to generate a NaiveBayes classifier from a set of training data.
NaiveBayesTrainer.Factory
PRAuxClassifier	Auxiliary model (q) for E-step/I-projection in PR training.
PRAuxClassifierOptimizable	Optimizable for training auxiliary model (q) for E-step/I-projection in PR training.
RandomAssignmentTrainer	A Classifier Trainer to be used with RandomClassifier.
RandomClassifier	A Classifier that will return a randomly selected class label.
RankMaxEnt	Rank Maximum Entropy classifier.
RankMaxEntTrainer	The trainer for a RankMaxEnt classifier.
Trial	Stores the results of classifying a collection of Instances, and provides many methods for evaluating the results.
Winnow	Classification methods of Winnow2 algorithm.
WinnowTrainer	An implementation of the training methods of a Winnow2 on-line classifier.

Package cc.mallet.classify Description

Classes for training and classifying instances. All classification techniques in MALLET are implemented as two classes: a trainer and a classifier. The trainer injests the training data and creates a classifier that holds the parameters set during training. The classifier applies those parameters to an Instance to produce a classification of the Instance.