gov.sandia.cognition.learning.algorithm.perceptron

Class OnlineVotedPerceptron

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<InputOutputPair<? extends InputType,OutputType>,ResultType>

gov.sandia.cognition.learning.algorithm.AbstractSupervisedBatchAndIncrementalLearner<Vectorizable,java.lang.Boolean,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>

gov.sandia.cognition.learning.algorithm.perceptron.OnlineVotedPerceptron

All Implemented Interfaces:

BatchAndIncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Boolean>,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Boolean>>,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>, IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Boolean>,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>, SupervisedBatchAndIncrementalLearner<Vectorizable,java.lang.Boolean,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>, SupervisedBatchLearner<Vectorizable,java.lang.Boolean,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>, SupervisedIncrementalLearner<Vectorizable,java.lang.Boolean,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>, VectorFactoryContainer, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@PublicationReference(title="Large Margin Classification Using the Perceptron Algorithm",
                      author={"Yoav Freund","Robert E. Schapire"},
                      year=1999,
                      type=Journal,
                      publication="Machine Learning",
                      pages={277,296},
                      url="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.8200")
public class OnlineVotedPerceptron
extends AbstractSupervisedBatchAndIncrementalLearner<Vectorizable,java.lang.Boolean,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>
implements VectorFactoryContainer

An online version of the Voted-Perceptron algorithm. It is similar to the typical Perceptron algorithm except that it creates multiple Perceptrons, and combines them together in a weighted vote. Whenever a mistake is made, a new Perceptron is created by modifying the previous one and given a weight of 1. When it gets an example correct, it simply increments the weight on the most recent one.

Since:

3.1

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected VectorFactory<?>	vectorFactory The factory to create weight vectors.

Constructor Summary

Constructors

Constructor and Description
OnlineVotedPerceptron() Creates a new OnlinePerceptron.
OnlineVotedPerceptron(VectorFactory<?> vectorFactory) Creates a new OnlinePerceptron with the given vector factory.

Method Summary

Modifier and Type	Method and Description
WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>	createInitialLearnedObject() Creates a new initial learned object, before any data is given.
static DefaultWeightedValue<LinearBinaryCategorizer>	getLastMember(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> ensemble) Gets the last member in the ensemble.
VectorFactory<?>	getVectorFactory() Gets the VectorFactory used to create the weight vector.
void	setVectorFactory(VectorFactory<?> vectorFactory) Sets the VectorFactory used to create the weight vector.
void	update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target, Vector input, boolean actual) The update method updates an object of ResultType using the given a new supervised input-output pair, using some form of "learning" algorithm.
void	update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target, Vectorizable input, java.lang.Boolean output) The update method updates an object of ResultType using the given a new supervised input-output pair, using some form of "learning" algorithm.

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractSupervisedBatchAndIncrementalLearner

update

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner

clone, learn, learn, update

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchAndIncrementalLearner

learn

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.learning.algorithm.IncrementalLearner

update

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Field Detail

vectorFactory

protected VectorFactory<?> vectorFactory

The factory to create weight vectors.

Constructor Detail

OnlineVotedPerceptron

public OnlineVotedPerceptron()

Creates a new OnlinePerceptron.

OnlineVotedPerceptron

public OnlineVotedPerceptron(VectorFactory<?> vectorFactory)

Creates a new OnlinePerceptron with the given vector factory.

Parameters:

vectorFactory - The vector factory to use to create the weight vectors.

Method Detail

createInitialLearnedObject

public WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> createInitialLearnedObject()

Description copied from interface: IncrementalLearner

Creates a new initial learned object, before any data is given.

Specified by:

createInitialLearnedObject in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Boolean>,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>

Returns:

The initial learned object.

update

public void update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target,
                   Vectorizable input,
                   java.lang.Boolean output)

Description copied from interface: SupervisedIncrementalLearner

The update method updates an object of ResultType using the given a new supervised input-output pair, using some form of "learning" algorithm.

Specified by:

update in interface SupervisedIncrementalLearner<Vectorizable,java.lang.Boolean,WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>>

Parameters:

target - The object to update.

input - The supervised input to learn from.

output - The supervised output to learn from.

update

public void update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target,
                   Vector input,
                   boolean actual)

The update method updates an object of ResultType using the given a new supervised input-output pair, using some form of "learning" algorithm.

Parameters:

target - The object to update.

input - The supervised input vector to learn from.

actual - The supervised output label to learn from.

getLastMember

public static DefaultWeightedValue<LinearBinaryCategorizer> getLastMember(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> ensemble)

Gets the last member in the ensemble. This is the one used by the algorithm.

Parameters:

ensemble - The ensemble to get the last member from.

Returns:

The last member in the ensemble, or null if it is empty.

getVectorFactory

public VectorFactory<?> getVectorFactory()

Gets the VectorFactory used to create the weight vector.

Specified by:

getVectorFactory in interface VectorFactoryContainer

Returns:

The VectorFactory used to create the weight vector.

setVectorFactory

public void setVectorFactory(VectorFactory<?> vectorFactory)

Sets the VectorFactory used to create the weight vector.

Parameters:

vectorFactory - The VectorFactory used to create the weight vector.