gov.sandia.cognition.learning.algorithm.perceptron

Class OnlineBinaryMarginInfusedRelaxedAlgorithm

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,LinearBinaryCategorizer>

gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner

gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner

gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

gov.sandia.cognition.learning.algorithm.perceptron.OnlineBinaryMarginInfusedRelaxedAlgorithm

All Implemented Interfaces:

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

@PublicationReference(author={"Koby Crammer","Yoram Singer"},
                      title="Ultraconservative Online Algorithms for Multiclass Problems",
                      year=2003,
                      type=Journal,
                      publication="Journal of Machine Learning Research",
                      pages={951,991},
                      url="http://portal.acm.org/citation.cfm?id=944936")
public class OnlineBinaryMarginInfusedRelaxedAlgorithm
extends AbstractLinearCombinationOnlineLearner

An implementation of the binary MIRA algorithm.

Since:

3.3.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_MIN_MARGIN The default minimum margin is 0.0.
static boolean	DEFAULT_UPDATE_BIAS MIRA does not use a bias by default.
protected double	minMargin The minimum margin to enforce.

Fields inherited from class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

updateBias

Fields inherited from class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner

vectorFactory

Constructor Summary

Constructors

Constructor and Description
OnlineBinaryMarginInfusedRelaxedAlgorithm() Creates a new OnlineBinaryMarginInfusedRelaxedAlgorithm with default parameters.
OnlineBinaryMarginInfusedRelaxedAlgorithm(double minMargin) Creates a new OnlineBinaryMarginInfusedRelaxedAlgorithm with the given minimum margin.
OnlineBinaryMarginInfusedRelaxedAlgorithm(double minMargin, VectorFactory<?> vectorFactory) Creates a new OnlineBinaryMarginInfusedRelaxedAlgorithm with the new minimum margin.

Method Summary

Modifier and Type	Method and Description
protected <InputType> double	computeUpdate(DefaultKernelBinaryCategorizer<InputType> target, InputType input, boolean actualCategory, double predicted) Compute the update weight in the linear case.
protected double	computeUpdate(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted) Compute the update weight in the linear case.
double	getMinMargin() Gets the minimum margin to enforce.
protected <InputType> void	initialize(DefaultKernelBinaryCategorizer<InputType> target, InputType input, boolean actualCategory) Initializes the kernel binary categorizer.
protected void	initialize(LinearBinaryCategorizer target, Vector input, boolean actualCategory) Initializes the linear binary categorizer.
void	setMinMargin(double minMargin) Gets the minimum margin to enforce.

Methods inherited from class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner

computeDecay, computeDecay, computeRescaling, computeRescaling, createInitialLearnedObject, isUpdateBias, setUpdateBias, update, update

Methods inherited from class gov.sandia.cognition.learning.algorithm.perceptron.AbstractKernelizableBinaryCategorizerOnlineLearner

createKernelLearner, learn, update, update, update

Methods inherited from class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner

createInitialLearnedObject, getVectorFactory, setVectorFactory, update

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.SupervisedIncrementalLearner

update

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

createInitialLearnedObject, update, update

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

clone

Field Detail

DEFAULT_UPDATE_BIAS

public static final boolean DEFAULT_UPDATE_BIAS

MIRA does not use a bias by default.

See Also:

Constant Field Values

DEFAULT_MIN_MARGIN

public static final double DEFAULT_MIN_MARGIN

The default minimum margin is 0.0.

See Also:

Constant Field Values

minMargin

protected double minMargin

The minimum margin to enforce. Must be non-negative.

Constructor Detail

OnlineBinaryMarginInfusedRelaxedAlgorithm

public OnlineBinaryMarginInfusedRelaxedAlgorithm()

Creates a new OnlineBinaryMarginInfusedRelaxedAlgorithm with default parameters.

OnlineBinaryMarginInfusedRelaxedAlgorithm

public OnlineBinaryMarginInfusedRelaxedAlgorithm(double minMargin)

Creates a new OnlineBinaryMarginInfusedRelaxedAlgorithm with the given minimum margin.

Parameters:

minMargin - The minimum margin to enforce. Must be non-negative.

OnlineBinaryMarginInfusedRelaxedAlgorithm

public OnlineBinaryMarginInfusedRelaxedAlgorithm(double minMargin,
                                                 VectorFactory<?> vectorFactory)

Creates a new OnlineBinaryMarginInfusedRelaxedAlgorithm with the new minimum margin.

Parameters:

minMargin - The minimum margin to enforce. Must be non-negative.

vectorFactory - The factory to use to create vectors.

Method Detail

getMinMargin

public double getMinMargin()

Gets the minimum margin to enforce. Any value less than or equal to this is considered an error for the algorithm.

Returns:

The minimum margin. Cannot be negative.

setMinMargin

public void setMinMargin(double minMargin)

Gets the minimum margin to enforce. Any value less than or equal to this is considered an error for the algorithm.

Parameters:

minMargin - The minimum margin. Cannot be negative.

initialize

protected void initialize(LinearBinaryCategorizer target,
                          Vector input,
                          boolean actualCategory)

Description copied from class: AbstractLinearCombinationOnlineLearner

Initializes the linear binary categorizer. Can be overridden. The default implementation just sets the weights to the zero vector.

Overrides:

initialize in class AbstractLinearCombinationOnlineLearner

Parameters:

target - The categorizer to initialize.

input - The first input seen.

actualCategory - The actual category of the first input.

computeUpdate

protected double computeUpdate(LinearBinaryCategorizer target,
                               Vector input,
                               boolean actualCategory,
                               double predicted)

Description copied from class: AbstractLinearCombinationOnlineLearner

Compute the update weight in the linear case. Must be implemented by subclasses.

Specified by:

computeUpdate in class AbstractLinearCombinationOnlineLearner

Parameters:

target - Target to compute the update for.

input - Input to use in computing the update.

actualCategory - The actual category of the input.

predicted - The predicted category of the input.

Returns:

The update weight for how much to add the input to the target. May be zero if no update is needed.

initialize

protected <InputType> void initialize(DefaultKernelBinaryCategorizer<InputType> target,
                                      InputType input,
                                      boolean actualCategory)

Description copied from class: AbstractLinearCombinationOnlineLearner

Initializes the kernel binary categorizer. Can be overridden. The default implementation does nothing.

Overrides:

initialize in class AbstractLinearCombinationOnlineLearner

Type Parameters:

InputType - The input value for learning.

Parameters:

target - The categorizer to initialize.

input - The first input seen.

actualCategory - The actual category of the first input.

computeUpdate

protected <InputType> double computeUpdate(DefaultKernelBinaryCategorizer<InputType> target,
                                           InputType input,
                                           boolean actualCategory,
                                           double predicted)

Description copied from class: AbstractLinearCombinationOnlineLearner

Compute the update weight in the linear case. Must be implemented by subclasses.

Specified by:

computeUpdate in class AbstractLinearCombinationOnlineLearner

Type Parameters:

InputType - The input value for learning.

Parameters:

target - Target to compute the update for.

input - Input to use in computing the update.

actualCategory - The actual category of the input.

predicted - The predicted category of the input.

Returns:

The update weight for how much to add the input to the target. May be zero if no update is needed.