Package com.imsl.datamining.supportvectormachine

Support vector machines.

See: Description

Class Summary

Class	Description
DataNode	Specifies a data node for a support vector machine.
Kernel	Abstract class to specify a kernel function for support vector machines.
LinearKernel	Specifies the linear kernel for support vector machines.
PolynomialKernel	Specifies the polynomial kernel for support vector machines.
RadialBasisKernel	Specifies the radial basis kernel for support vector machines.
SigmoidKernel	Specifies the sigmoid kernel for support vector machines.
SupportVectorMachine	Abstract class for generating a support vector machine.
SVClassification	Specifies a support vector machine for classification (SVC).
SVModel	Class to contain model estimates after training a support vector machine.
SVOneClass	Specifies a support vector machine for the one class problem.
SVRegression	Specifies a support vector machine for regression (SVR).

Exception Summary

Exception	Description
SupportVectorMachine.ReflectiveOperationException	Class that wraps exceptions thrown by reflective operations in core reflection.

Package com.imsl.datamining.supportvectormachine Description

Support vector machines. This package is based on the LIBSVM version 3.1 library for support vector machines. Class SVRegression implements the \(\nu\)- and \(\epsilon\) formulations for regression: \(\nu\)-SVR and \(\epsilon\)-SVR. Class SVClassification contains implementations of the C- and \(\nu\) models for classification, C-SVC and \(\nu\)-SVC. Class SVOneClass estimates the support of a high-dimensional distribution, using the One-class SVM. All three classes are subclasses of SupportVectorMachine. This parent class contains methods which are available for all SVM formulations. Furthermore, all SVMs may use different kernels for optimization and the calculation of predictions. The kernels are implemented in classes LinearKernel, PolynomialKernel, RadialBasisKernel and SigmoidKernel.