jsat.classifiers.svm

Class SVMnoBias

java.lang.Object

jsat.classifiers.svm.SupportVectorLearner

jsat.classifiers.svm.SVMnoBias

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, BinaryScoreClassifier, Classifier

public class SVMnoBias
extends SupportVectorLearner
implements BinaryScoreClassifier

This class implements a version of the Support Vector Machine without a bias term. In addition, the current implementation requires that the Kernel Trick used be a normalized kernel. If the given kernel is not normalized, this class will automatically wrap it to become normalized.

Because there is no bias term, this class should never be used with the Linear kernel. But for the more common RBF kernel the lack of bias term should have minimal impact on accuracy.

See: Steinwart, I., Hush, D., & Scovel, C. (2011). Training SVMs Without Offset. The Journal of Machine Learning Research, 12, 141–202.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class jsat.classifiers.svm.SupportVectorLearner

SupportVectorLearner.CacheMode

Constructor Summary

Constructors

Constructor and Description
SVMnoBias(KernelTrick kf) Creates a new SVM object that uses no cache mode.
SVMnoBias(SVMnoBias toCopy)

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
SVMnoBias	clone()
double	getC() Returns the soft margin complexity parameter of the SVM
double	getScore(DataPoint dp) Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
double	getTolerance() Returns the solution tolerance
void	setC(double C) Sets the complexity parameter of SVM.
void	setKernel(KernelTrick kernel) Sets the kernel trick to use
void	setTolerance(double tolerance) Sets the tolerance for the solution.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class jsat.classifiers.svm.SupportVectorLearner

getCacheMode, getCacheValue, getKernel, setCacheMode, setCacheSize, setCacheValue

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train

Constructor Detail

SVMnoBias

public SVMnoBias(KernelTrick kf)

Creates a new SVM object that uses no cache mode.

Parameters:

kf - the kernel trick to use

SVMnoBias

public SVMnoBias(SVMnoBias toCopy)

Method Detail

setKernel

public void setKernel(KernelTrick kernel)

Description copied from class: SupportVectorLearner

Sets the kernel trick to use

Overrides:

setKernel in class SupportVectorLearner

Parameters:

kernel - the kernel trick to use

getScore

public double getScore(DataPoint dp)

Description copied from interface: BinaryScoreClassifier

Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.

Specified by:

getScore in interface BinaryScoreClassifier

Parameters:

dp - the data point to predict the class label of

Returns:

the score for the given data point

clone

public SVMnoBias clone()

Specified by:

clone in interface BinaryScoreClassifier

Specified by:

clone in interface Classifier

Overrides:

clone in class java.lang.Object

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Specified by:

classify in interface Classifier

Parameters:

data - the data point to classify

Returns:

the results of the classification.

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Classifier

Returns:

true if the model supports weighted data, false otherwise

setC

public void setC(double C)

Sets the complexity parameter of SVM. The larger the C value the harder the margin SVM will attempt to find. Lower values of C allow for more misclassification errors.

Parameters:

C - the soft margin parameter

getC

public double getC()

Returns the soft margin complexity parameter of the SVM

Returns:

the complexity parameter of the SVM

setTolerance

public void setTolerance(double tolerance)

Sets the tolerance for the solution. Higher values converge to worse solutions, but do so faster

Parameters:

tolerance - the tolerance for the solution

getTolerance

public double getTolerance()

Returns the solution tolerance

Returns:

the solution tolerance