Package cc.mallet.classify

Classes for training and classifying instances.


Interface Summary
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
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.
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.
C45Trainer A C4.5 decision tree learner, approximtely.
Classification The result of classifying a single instance.
Classifier Abstract parent of all Classifiers.
ClassifierEnsemble Classifer for an ensemble of classifers, combined with learned weights.
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.
DecisionTree Decision Tree classifier.
DecisionTreeTrainer A decision tree learner, roughly ID3, but only to a fixed given depth in all branches.
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.
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.
NaiveBayes A classifier that classifies instances according to the NaiveBayes method.
NaiveBayesTrainer Class used to generate a NaiveBayes classifier from a set of training data.
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.
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.