Uses of Interface
edu.umass.cs.mallet.base.classify.ClassifierEvaluating

Packages that use ClassifierEvaluating

Package	Description
edu.umass.cs.mallet.base.classify

Uses of ClassifierEvaluating in edu.umass.cs.mallet.base.classify

Classes in edu.umass.cs.mallet.base.classify that implement ClassifierEvaluating

Modifier and Type	Class and Description
class	AbstractClassifierEvaluating Created: Apr 13, 2005
class	AccuracyEvaluator

Methods in edu.umass.cs.mallet.base.classify with parameters of type ClassifierEvaluating

Modifier and Type	Method and Description
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator) Return a new classifier tuned using four arguments.
Classifier	NaiveBayesTrainer.incrementalTrain(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Create a NaiveBayes classifier from a set of training data and the previous state of the trainer.
abstract Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Return a new classifier tuned using the five arguments.
Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator)
Classifier	WinnowTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Trains winnow on the instance list, updating weights according to errors
Classifier	NaiveBayesTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Create a NaiveBayes classifier from a set of training data.
Classifier	MaxEntTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	MCMaxEntTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	FeatureSelectingClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	DecisionTreeTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	ConfidencePredictingClassifierTrainer.train(InstanceList trainList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
abstract Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Return a new classifier tuned using the three arguments.
Classifier	C45Trainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	BalancedWinnowTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Trains the classifier on the instance list, updating class weight vectors as appropriate
Classifier	BaggingTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier)
Classifier	AdaBoostTrainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Boosting method that resamples instances using their weights
Classifier	AdaBoostM2Trainer.train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Boosting method that resamples instances using their weights
Classifier	MaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction) Trains a maximum entropy model using feature selection and feature induction (adding conjunctions of features as new features).
Classifier	MCMaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction) Trains a maximum entropy model using feature selection and feature induction (adding conjunctions of features as new features).
Classifier	MaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, MaxEnt maxent, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, String gainName) Like the other version of trainWithFeatureInduction, but allows some default options to be changed.
Classifier	MCMaxEntTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, ClassifierEvaluating evaluator, MCMaxEnt maxent, int totalIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, String gainName) Like the other version of trainWithFeatureInduction, but allows some default options to be changed.