|
||||||||||
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
See:
Description
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. |
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. |
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. |
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. |
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.
|
||||||||||
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |