Package cc.mallet.classify

Class BalancedWinnowTrainer

java.lang.Object

cc.mallet.classify.ClassifierTrainer<BalancedWinnow>

cc.mallet.classify.BalancedWinnowTrainer

All Implemented Interfaces:

Boostable, java.io.Serializable

public class BalancedWinnowTrainer
extends ClassifierTrainer<BalancedWinnow>
implements Boostable, java.io.Serializable

An implementation of the training methods of a BalancedWinnow on-line classifier. Given a labeled instance (x, y) the algorithm computes dot(x, wi), for w1, ... , wc where wi is the weight vector for class i. The instance is classified as class j if the value of dot(x, wj) is the largest among the c dot products.

The weight vectors are updated whenever the the classifier makes a mistake or just barely got the correct answer (highest dot product is within delta percent higher than the second highest). Suppose the classifier guessed j and answer was j'. For each feature i that is present, multiply w_ji by (1-epsilon) and multiply w_j'i by (1+epsilon)

The above procedure is done multiple times to the training examples (default is 5), and epsilon is cut by the cooling rate at each iteration (default is cutting epsilon by half).

Author:

Gary Huang ghuang@cs.umass.edu

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class cc.mallet.classify.ClassifierTrainer

ClassifierTrainer.ByActiveLearning<C extends Classifier>, ClassifierTrainer.ByIncrements<C extends Classifier>, ClassifierTrainer.ByInstanceIncrements<C extends Classifier>, ClassifierTrainer.ByOptimization<C extends Classifier>, ClassifierTrainer.Factory<CT extends ClassifierTrainer<? extends Classifier>>

Field Summary

Fields

Modifier and Type	Field	Description
static double	DEFAULT_COOLING_RATE	0.5
static double	DEFAULT_DELTA	0.1
static double	DEFAULT_EPSILON	0.5
static int	DEFAULT_MAX_ITERATIONS	30

Fields inherited from class cc.mallet.classify.ClassifierTrainer

finishedTraining, validationSet

Constructor Summary

Constructors

Constructor	Description
BalancedWinnowTrainer()	Default constructor.
BalancedWinnowTrainer(double epsilon, double delta, int maxIterations, double coolingRate)

Method Summary

Modifier and Type	Method	Description
BalancedWinnow	getClassifier()
BalancedWinnow	train(InstanceList trainingList)	Trains the classifier on the instance list, updating class weight vectors as appropriate

Methods inherited from class cc.mallet.classify.ClassifierTrainer

getValidationInstances, isFinishedTraining, setValidationInstances

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_EPSILON

public static final double DEFAULT_EPSILON

0.5

See Also:

Constant Field Values

DEFAULT_DELTA

public static final double DEFAULT_DELTA

0.1

See Also:

Constant Field Values

DEFAULT_MAX_ITERATIONS

public static final int DEFAULT_MAX_ITERATIONS

30

See Also:

Constant Field Values

DEFAULT_COOLING_RATE

public static final double DEFAULT_COOLING_RATE

0.5

See Also:

Constant Field Values

Constructor Detail

BalancedWinnowTrainer

public BalancedWinnowTrainer()

Default constructor. Sets all features to defaults.

BalancedWinnowTrainer

public BalancedWinnowTrainer(double epsilon,
                             double delta,
                             int maxIterations,
                             double coolingRate)

Parameters:

epsilon - percentage by which to increase/decrease weight vectors when an example is misclassified.

delta - percentage by which the highest (and correct) dot product should exceed the second highest dot product before we consider an example to be correctly classified (margin width) when adjusting weights.

maxIterations - maximum number of times to loop through training examples.

coolingRate - percentage of epsilon to decrease after each iteration

Method Detail

getClassifier

public BalancedWinnow getClassifier()

Specified by:

getClassifier in class ClassifierTrainer<BalancedWinnow>

train

public BalancedWinnow train(InstanceList trainingList)

Trains the classifier on the instance list, updating class weight vectors as appropriate

Specified by:

train in class ClassifierTrainer<BalancedWinnow>

Parameters:

trainingList - Instance list to be trained on

Returns:

Classifier object containing learned weights