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

java.lang.Object
  extended byedu.umass.cs.mallet.base.classify.ClassifierTrainer
      extended byedu.umass.cs.mallet.base.classify.AdaBoostTrainer

public class AdaBoostTrainer
extends ClassifierTrainer

This version of AdaBoost should be used only for binary classification. Use AdaBoost.M2 for multi-class problems.

Robert E. Schapire. "A decision-theoretic generalization of on-line learning and an application to boosting" In Journal of Computer and System Sciences http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/FreundSc95.ps


Constructor Summary
AdaBoostTrainer(ClassifierTrainer weakLearner)
           
AdaBoostTrainer(ClassifierTrainer weakLearner, int numRounds)
           
 
Method Summary
 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

AdaBoostTrainer

public AdaBoostTrainer(ClassifierTrainer weakLearner,
                       int numRounds)

AdaBoostTrainer

public AdaBoostTrainer(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.