edu.umass.cs.mallet.base.classify

Class AdaBoostM2Trainer

java.lang.Object

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

edu.umass.cs.mallet.base.classify.AdaBoostM2Trainer

public class AdaBoostM2Trainer
extends ClassifierTrainer

This version of AdaBoost can handle multi-class problems. For binary classification, can also use AdaBoostTrainer.

Yoav Freund and Robert E. Schapire "Experiments with a New Boosting Algorithm" In Journal of Machine Learning: Proceedings of the 13th International Conference, 1996 http://www.cs.princeton.edu/~schapire/papers/FreundSc96b.ps.Z

Author:

Gary Huang ghuang@cs.umass.edu

Constructor Summary

Constructors

Constructor and Description
AdaBoostM2Trainer(ClassifierTrainer weakLearner)
AdaBoostM2Trainer(ClassifierTrainer weakLearner, int numRounds)

Method Summary

Modifier and Type	Method and Description
Classifier	train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Boosting method that resamples instances using their weights

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

AdaBoostM2Trainer

public AdaBoostM2Trainer(ClassifierTrainer weakLearner,
                         int numRounds)

AdaBoostM2Trainer

public AdaBoostM2Trainer(ClassifierTrainer weakLearner)

Method Detail

train

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

Boosting method that resamples instances using their weights

Specified by:

train in class ClassifierTrainer

Parameters:

trainingList - 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.