Uses of Class
com.imsl.datamining.supportvectormachine.Kernel

Packages that use Kernel

Package	Description
com.imsl.datamining.supportvectormachine	Support vector machines.

Uses of Kernel in com.imsl.datamining.supportvectormachine

Subclasses of Kernel in com.imsl.datamining.supportvectormachine

Modifier and Type	Class	Description
class	LinearKernel	Specifies the linear kernel for support vector machines.
class	PolynomialKernel	Specifies the polynomial kernel for support vector machines.
class	RadialBasisKernel	Specifies the radial basis kernel for support vector machines.
class	SigmoidKernel	Specifies the sigmoid kernel for support vector machines.

Methods in com.imsl.datamining.supportvectormachine that return Kernel

Modifier and Type	Method	Description
abstract Kernel	Kernel.clone()	Returns a clone of this object.
protected Kernel	SupportVectorMachine.getKernel()	Returns the kernel object being used in the optimization.

Methods in com.imsl.datamining.supportvectormachine with parameters of type Kernel

Modifier and Type	Method	Description
protected abstract SVModel	SupportVectorMachine.optimize(DataNode[][] x, double[] y, double[] w, int len, Kernel kernel)	Abstract method to perform the support vector machine optimization.
protected SVModel	SVClassification.optimize(DataNode[][] x, double[] y, double[] w, int len, Kernel kernel)	Performs the classification support vector machine optimization problem.
protected SVModel	SVOneClass.optimize(DataNode[][] x, double[] y, double[] w, int len, Kernel kernel)	Performs the one class support vector machine optimization problem.
protected SVModel	SVRegression.optimize(DataNode[][] x, double[] y, double[] w, int len, Kernel kernel)	Performs the regression support vector machine optimization problem.
void	SupportVectorMachine.setKernel(Kernel kernel)	Sets the kernel to be used in the optimization.

Constructors in com.imsl.datamining.supportvectormachine with parameters of type Kernel

Modifier	Constructor	Description
	SupportVectorMachine(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a support vector machine for a single response variable and multiple predictor variables.
	SVClassification(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a support vector machine for classification (SVC).
	SVOneClass(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a one class support vector machine.
	SVRegression(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType, Kernel k)	Constructs a support vector machine for regression (SVR).