edu.umass.cs.mallet.base.classify

Class ConfidencePredictingClassifierTrainer

java.lang.Object

edu.umass.cs.mallet.base.classify.ClassifierTrainer

edu.umass.cs.mallet.base.classify.ConfidencePredictingClassifierTrainer

All Implemented Interfaces:

Boostable

public class ConfidencePredictingClassifierTrainer
extends ClassifierTrainer
implements Boostable

Constructor Summary

Constructors

Constructor and Description
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer)
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer, Pipe confidencePredictingPipe)

Method Summary

Modifier and Type	Method and Description
Classifier	train(InstanceList trainList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Return a new classifier tuned using the three arguments.

Methods inherited from class edu.umass.cs.mallet.base.classify.ClassifierTrainer

main, toString, train, train, train, train

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

ConfidencePredictingClassifierTrainer

public ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer,
                                             Pipe confidencePredictingPipe)

ConfidencePredictingClassifierTrainer

public ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer)

Method Detail

train

public Classifier train(InstanceList trainList,
                        InstanceList validationList,
                        InstanceList testSet,
                        ClassifierEvaluating evaluator,
                        Classifier initialClassifier)

Description copied from class: ClassifierTrainer

Return a new classifier tuned using the three arguments.

Specified by:

train in class ClassifierTrainer

Parameters:

trainList - examples used to set parameters.

validationList - examples used to tune meta-parameters. May be null.

testSet - examples not examined at all for training, but passed on to diagnostic routines. May be null.

initialClassifier - training process may start from here. The parameters of the initialClassifier are not modified. May be null.