Package edu.udo.cs.yale.operator.learner.kernel.evosvm

Implementations of SVMs which makes use of general purpose optimization methods, e.g. evolutionary strategies or particle swarm optimization.

See:
Description

Interface Summary

OptimizationFunction

Subclasses might implement the optimization function in different ways, e.g.

Class Summary

ClassificationOptimizationFunction	This function must be maximized for the search for an optimal hyperplane for classification.
DotKernel	Returns the simple inner product of both examples.
EpanechnikovKernel	Returns the value of the Epanechnikov kernel of both examples.
EvoOptimization	Evolutionary Strategy approach for SVM optimization.
EvoSVM	This is a SVM implementation using an evolutionary algorithm (ES) to solve the dual optimization problem of a SVM.
EvoSVMModel	The model for the evolutionary SVM.
GaussianCombinationKernel	Returns the value of the Gaussian combination kernel of both examples.
Kernel	Returns the distance of two examples.
MultiquadricKernel	Returns the value of the Multiquadric kernel of both examples.
PolynomialKernel	Returns the value of a Polynomial kernel of both examples.
PSOSVM	This is a SVM implementation using a particle swarm optimization (PSO) approach to solve the dual optimization problem of a SVM.
PSOSVMOptimization	PSO approach for SVM optimization.
RBFKernel	Returns the value of the RBF kernel of both examples.
RegressionOptimizationFunction	This function must be maximized for the search for an optimal hyperplane for regression.
SigmoidKernel	Returns the value of a Sigmoid kernel of both examples.
SupportVector	Holds all information of a support vector, i.e. the attribute values, the label, and the alpha.

Package edu.udo.cs.yale.operator.learner.kernel.evosvm Description

Implementations of SVMs which makes use of general purpose optimization methods, e.g. evolutionary strategies or particle swarm optimization.