edu.umass.cs.mallet.base.classify

Class WinnowTrainer

java.lang.Object

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

edu.umass.cs.mallet.base.classify.WinnowTrainer

public class WinnowTrainer
extends ClassifierTrainer

An implementation of the training methods of a Winnow2 on-line classifier. Given an instance xi, the algorithm computes Sum(xi*wi), where wi is the weight for that feature in the given class. If the Sum is greater than some threshold theta, then the classifier guess true for that class. Only when the classifier makes a mistake are the weights updated in one of two steps: Promote: guessed 0 and answer was 1. Multiply all weights of present features by alpha. Demote: guessed 1 and answer was 0. Divide all weights of present features by beta. Limitations: Winnow2 only considers binary feature vectors (i.e. whether or not the feature is present, not its value).

Constructor Summary

Constructors

Constructor and Description
WinnowTrainer() Default constructor.
WinnowTrainer(double a, double b) Sets alpha and beta and default value for theta
WinnowTrainer(double a, double b, double nfact) Sets alpha, beta, and nfactor

Method Summary

Modifier and Type	Method and Description
Classifier	train(InstanceList trainingList, InstanceList validationList, InstanceList testSet, ClassifierEvaluating evaluator, Classifier initialClassifier) Trains winnow on the instance list, updating weights according to errors

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

WinnowTrainer

public WinnowTrainer()

Default constructor. Sets all features to defaults.

WinnowTrainer

public WinnowTrainer(double a,
                     double b)

Sets alpha and beta and default value for theta

Parameters:

a - alpha value

b - beta value

WinnowTrainer

public WinnowTrainer(double a,
                     double b,
                     double nfact)

Sets alpha, beta, and nfactor

Parameters:

a - alpha value

b - beta value

nfact - nfactor value

Method Detail

train

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

Trains winnow on the instance list, updating weights according to errors

Specified by:

train in class ClassifierTrainer

Parameters:

ilist - Instance list to be trained on

Returns:

Classifier object containing learned weights