Uses of Class
edu.umass.cs.mallet.base.classify.Classifier

Packages that use Classifier

Package	Description
edu.umass.cs.mallet.base.classify
edu.umass.cs.mallet.base.classify.evaluate
edu.umass.cs.mallet.base.pipe
edu.umass.cs.mallet.projects.seg_plus_coref.anaphora
edu.umass.cs.mallet.projects.seg_plus_coref.condclust.cluster
edu.umass.cs.mallet.projects.seg_plus_coref.condclust.pipe
edu.umass.cs.mallet.projects.seg_plus_coref.condclust.tui
edu.umass.cs.mallet.share.upenn

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

Subclasses of Classifier in edu.umass.cs.mallet.base.classify

Modifier and Type	Class and Description
class	AdaBoost AdaBoost Robert E.
class	AdaBoostM2 AdaBoostM2
class	BaggingClassifier
class	BalancedWinnow Classification methods of BalancedWinnow algorithm.
class	C45 A C4.5 Decision Tree classifier.
class	ConfidencePredictingClassifier
class	DecisionTree Decision Tree classifier.
class	MaxEnt Maximum Entropy classifier.
class	MCMaxEnt Maximum Entropy classifier.
class	NaiveBayes A classifier that classifies instances according to the NaiveBayes method.
class	Winnow Classification methods of Winnow2 algorithm.

Methods in edu.umass.cs.mallet.base.classify that return Classifier

Modifier and Type	Method and Description
Classifier	Trial.getClassifier()
Classifier	Classification.getClassifier()
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet) Return a new classifier tuned from an instanceList
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet) Return a new classifier tuned using two arguments.
Classifier	IncrementalClassifierTrainer.incrementalTrain(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet) Return a new classifier tuned using three arguments.
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)
Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet)
Classifier	ClassifierTrainer.train(InstanceList trainingSet, InstanceList validationSet, InstanceList testSet)
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.

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

Modifier and Type	Method and Description
boolean	ClassifierEvaluating.evaluate(Classifier classifier, boolean finishedTraining, int iteration, double cost, InstanceList training, InstanceList validation, InstanceList testing) Training will terminate if "false" is returned.
boolean	AccuracyEvaluator.evaluate(Classifier classifier, boolean finishedTraining, int iteration, double cost, InstanceList training, InstanceList validation, InstanceList testing)
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	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

Constructors in edu.umass.cs.mallet.base.classify with parameters of type Classifier

Constructor and Description
AdaBoost(Pipe instancePipe, Classifier[] weakClassifiers, double[] alphas)
AdaBoostM2(Pipe instancePipe, Classifier[] weakClassifiers, double[] alphas)
BaggingClassifier(Pipe instancePipe, Classifier[] baggedClassifiers)
Classification(Instance instance, Classifier classifier, Labeling labeling)
ConfidencePredictingClassifier(Classifier underlyingClassifier, Classifier confidencePredictingClassifier)
Trial(Classifier c, InstanceList ilist)

Uses of Classifier in edu.umass.cs.mallet.base.classify.evaluate

Constructors in edu.umass.cs.mallet.base.classify.evaluate with parameters of type Classifier

Constructor and Description
AccuracyCoverage(Classifier C, InstanceList ilist, int numBuckets, String title)
AccuracyCoverage(Classifier C, InstanceList ilist, String title)

Uses of Classifier in edu.umass.cs.mallet.base.pipe

Subclasses of Classifier in edu.umass.cs.mallet.base.pipe

Modifier and Type	Class and Description
static class	AddClassifierTokenPredictions.TokenClassifiers This inner class represents the trained token classifiers.

Uses of Classifier in edu.umass.cs.mallet.projects.seg_plus_coref.anaphora

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.anaphora with parameters of type Classifier

Modifier and Type	Method and Description
static Clustering	TUIGraph.getMortonClustering(List trainingMentionPairs, Classifier classifier)

Constructors in edu.umass.cs.mallet.projects.seg_plus_coref.anaphora with parameters of type Classifier

Constructor and Description
AccuracyCoverage(Classifier classifier, InstanceList instances)

Uses of Classifier in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.cluster

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.cluster that return Classifier

Modifier and Type	Method and Description
Classifier	ConditionalClusterer.getClassifier()

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.cluster with parameters of type Classifier

Modifier and Type	Method and Description
Collection	ConditionalClusterer.clusterPapersAndVenues(ArrayList _papers, ArrayList _venues, Collection paperTrueClustering, Collection venueTrueClustering, Classifier paperClusterClassifier, Classifier venueClusterClassifier, Random r) Cluster papers and venues jointly.

Constructors in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.cluster with parameters of type Classifier

Constructor and Description
ConditionalClusterer(Pipe _pipe, Classifier _classifier)
ConditionalClusterer(Pipe _pipe, Classifier _classifier, double _threshold)

Uses of Classifier in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.pipe

Constructors in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.pipe with parameters of type Classifier

Constructor and Description
AllLinks(Classifier _classifier)
AllLinks(Classifier _classifier, boolean _includePairwiseFeatures)
AllLinks(Classifier _classifier, boolean _includePairwiseFeatures, int n)
AverageLink(Classifier _classifier)
ClosestSingleLink(Classifier _classifier)
ClosestSingleLink(Classifier _classifier, boolean _includePairwiseFeatures)
ClusterHomogeneity(Classifier _classifier)
FarthestSingleLink(Classifier _classifier)
NNegativeNodes(Classifier _classifier)
NNegativeNodes(Classifier _classifier, int _n)
PaperClusterPrediction(Classifier _classifier)
VenueClusterPrediction(Classifier _classifier)

Uses of Classifier in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.tui

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.tui that return Classifier

Modifier and Type	Method and Description
Classifier	PairwiseClustererTUI.trainPairwiseClassifier(ArrayList[] nodes, Pipe p)

Methods in edu.umass.cs.mallet.projects.seg_plus_coref.condclust.tui with parameters of type Classifier

Modifier and Type	Method and Description
Pipe	PairwiseClustererTUI.getPipe(Classifier pairwiseClassifier) Create pipe for conditionalClusterer

Uses of Classifier in edu.umass.cs.mallet.share.upenn

Methods in edu.umass.cs.mallet.share.upenn that return Classifier

Modifier and Type	Method and Description
static Classifier	MaxEntShell.load(File modelFile) Load a classifier from a file.
static Classifier	MaxEntShell.train(PipeInputIterator data, double var, File save) Train a maxent classifier.
static Classifier	MaxEntShell.train(String[][] features, String[] labels, double var, File save) Train a maxent classifier.

Methods in edu.umass.cs.mallet.share.upenn with parameters of type Classifier

Modifier and Type	Method and Description
static Classification[]	MaxEntShell.classify(Classifier classifier, PipeInputIterator data) Compute the maxent classifications for unlabeled instances given by an iterator.
static Classification	MaxEntShell.classify(Classifier classifier, String[] features) Compute the maxent classification of an instance.
static Classification[]	MaxEntShell.classify(Classifier classifier, String[][] features) Compute the maxent classifications of an array of instances
static double	MaxEntShell.test(Classifier classifier, PipeInputIterator data) Test a maxent classifier.
static double	MaxEntShell.test(Classifier classifier, String[][] features, String[] labels) Test a maxent classifier.