edu.udo.cs.yale.operator.learner.kernel.evosvm
Class EvoSVM

java.lang.Object
  edu.udo.cs.yale.operator.Operator
      edu.udo.cs.yale.operator.learner.AbstractLearner
          edu.udo.cs.yale.operator.learner.kernel.evosvm.EvoSVM

All Implemented Interfaces:

ConfigurationListener, Learner

public class EvoSVM
extends AbstractLearner

This is a SVM implementation using an evolutionary algorithm (ES) to solve the dual optimization problem of a SVM. It turns out that on many datasets this simple implementation is as fast and accurate as the usual SVM implementations. In addition, it is also capable of learning with Kernels which are not positive semi-definite and can also be used for multi-objective learning which makes the selection of C unecessary before learning.

Mierswa, Ingo. Evolutionary Learning with Kernels: A Generic Solution for Large Margin Problems. In Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2006), 2006.

Version:

$Id: EvoSVM.java,v 1.25 2006/09/09 21:38:21 ingomierswa Exp $

Author:

Ingo Mierswa

Keywords:

SVM

Field Summary

private EvoOptimization

optimization The optimization procedure.

Constructor Summary

EvoSVM(OperatorDescription description)
Creates a new SVM which uses an Evolutionary Strategy approach for optimization.

Method Summary

PerformanceVector	getOptimizationPerformance() Returns the optimization performance of the best result.
java.util.List<ParameterType>	getParameterTypes() Returns a list of ParameterTypes describing the parameters of this operator.
Model	learn(ExampleSet exampleSet) Learns and returns a model.
boolean	shouldDeliverOptimizationPerformance() Returns the value of the corresponding parameter.
boolean	supportsCapability(LearnerCapability lc) Returns true for numerical attributes, binominal classes, and numerical target attributes.

Methods inherited from class edu.udo.cs.yale.operator.learner.AbstractLearner

apply, checkLearnerCapabilities, getEstimatedPerformance, getInputClasses, getInputDescription, getOutputClasses, getWeights, shouldCalculateWeights, shouldEstimatePerformance

Methods inherited from class edu.udo.cs.yale.operator.Operator

addError, addValue, addWarning, apply, checkDeprecations, checkIO, checkProperties, clearErrorList, cloneOperator, createExperimentTree, createExperimentTree, createFromXML, createMarkedExperimentTree, delete, experimentFinished, experimentStarts, getAddOnlyAdditionalOutput, getApplyCount, getDeliveredOutputClasses, getDeprecationInfo, getDesiredInputClasses, getErrorList, getExperiment, getInnerOperatorsXML, getInput, getInput, getInput, getIOContainerForInApplyLoopBreakpoint, getName, getNumberOfSteps, getOperatorClassName, getOperatorDescription, getParameter, getParameterAsBoolean, getParameterAsColor, getParameterAsDouble, getParameterAsFile, getParameterAsInt, getParameterAsString, getParameterList, getParameters, getParameterType, getParent, getStartTime, getStatus, getUserDescription, getValue, getValues, getXML, hasBreakpoint, hasBreakpoint, hasInput, inApplyLoop, isEnabled, isParameterSet, logMessage, performAdditionalChecks, register, remove, rename, resume, setBreakpoint, setEnabled, setExperiment, setInput, setListParameter, setOperatorParameters, setParameter, setParameters, setParent, setUserDescription, toString, writeXML

Methods inherited from class java.lang.Object

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

Methods inherited from interface edu.udo.cs.yale.operator.learner.Learner

getName

Field Detail

optimization

private EvoOptimization optimization

The optimization procedure.

Constructor Detail

EvoSVM

public EvoSVM(OperatorDescription description)

Creates a new SVM which uses an Evolutionary Strategy approach for optimization.

Method Detail

shouldDeliverOptimizationPerformance

public boolean shouldDeliverOptimizationPerformance()

Returns the value of the corresponding parameter.

Overrides:

shouldDeliverOptimizationPerformance in class AbstractLearner

getOptimizationPerformance

public PerformanceVector getOptimizationPerformance()

Returns the optimization performance of the best result. This method must be called after training, not before.

Overrides:

getOptimizationPerformance in class AbstractLearner

learn

public Model learn(ExampleSet exampleSet)
            throws OperatorException

Learns and returns a model.

Throws:

OperatorException

supportsCapability

public boolean supportsCapability(LearnerCapability lc)

Returns true for numerical attributes, binominal classes, and numerical target attributes.

getParameterTypes

public java.util.List<ParameterType> getParameterTypes()

Description copied from class: Operator

Returns a list of ParameterTypes describing the parameters of this operator. The default implementation returns an empty list if no input objects can be retained and special parameters for those input objects which can be prevented from being consumed.

Overrides:

getParameterTypes in class Operator