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

java.lang.Object
  extended byedu.umass.cs.mallet.base.classify.ClassifierTrainer
      extended byedu.umass.cs.mallet.base.classify.ConfidencePredictingClassifierTrainer
All Implemented Interfaces:
Boostable

public class ConfidencePredictingClassifierTrainer
extends ClassifierTrainer
implements Boostable


Constructor Summary
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer)
           
ConfidencePredictingClassifierTrainer(ClassifierTrainer underlyingClassifierTrainer, Pipe confidencePredictingPipe)
           
 
Method Summary
 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.