Uses of Class
weka.classifiers.functions.supportVector.Kernel

Packages that use Kernel

Package	Description
weka.classifiers.functions
weka.classifiers.functions.supportVector
weka.filters.unsupervised.attribute

Uses of Kernel in weka.classifiers.functions

Methods in weka.classifiers.functions that return Kernel

Modifier and Type	Method	Description
Kernel	GaussianProcesses.getKernel()	Gets the kernel to use.
Kernel	SMO.BinarySMO.getKernel()	Returns the kernel to use
Kernel	SMO.getKernel()	Returns the kernel to use
Kernel	SMOreg.getKernel()	Returns the kernel to use

Methods in weka.classifiers.functions with parameters of type Kernel

Modifier and Type	Method	Description
void	GaussianProcesses.setKernel(Kernel value)	Sets the kernel to use.
void	SMO.BinarySMO.setKernel(Kernel value)	sets the kernel to use
void	SMO.setKernel(Kernel value)	sets the kernel to use
void	SMOreg.setKernel(Kernel value)	sets the kernel to use

Uses of Kernel in weka.classifiers.functions.supportVector

Subclasses of Kernel in weka.classifiers.functions.supportVector

Modifier and Type	Class	Description
class	CachedKernel	Base class for RBFKernel and PolyKernel that implements a simple LRU.
class	NormalizedPolyKernel	The normalized polynomial kernel. K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
class	PolyKernel	The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
class	PrecomputedKernelMatrixKernel	This kernel is based on a static kernel matrix that is read from a file.
class	Puk	The Pearson VII function-based universal kernel. For more information see: B.
class	RBFKernel	The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2) Valid options are:
class	StringKernel	Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2]. For more information, see Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.

Methods in weka.classifiers.functions.supportVector that return Kernel

Modifier and Type	Method	Description
static Kernel	Kernel.forName(String kernelName, String[] options)	Creates a new instance of a kernel given it's class name and (optional) arguments to pass to it's setOptions method.
Kernel	CheckKernel.getKernel()	Get the kernel being tested
static Kernel[]	Kernel.makeCopies(Kernel model, int num)	Creates a given number of deep or shallow (if the kernel implements Copyable) copies of the given kernel using serialization.
static Kernel	Kernel.makeCopy(Kernel kernel)	Creates a shallow copy of the kernel (if it implements Copyable) otherwise a deep copy using serialization.

Methods in weka.classifiers.functions.supportVector with parameters of type Kernel

Modifier and Type	Method	Description
static String	KernelEvaluation.evaluate(Kernel Kernel, String[] options)	Evaluates the Kernel with the given commandline options and returns the evaluation string.
String	KernelEvaluation.evaluate(Kernel kernel, Instances data)	Evaluates the Kernel with the given commandline options and returns the evaluation string.
static Kernel[]	Kernel.makeCopies(Kernel model, int num)	Creates a given number of deep or shallow (if the kernel implements Copyable) copies of the given kernel using serialization.
static Kernel	Kernel.makeCopy(Kernel kernel)	Creates a shallow copy of the kernel (if it implements Copyable) otherwise a deep copy using serialization.
void	CheckKernel.setKernel(Kernel value)	Set the lernel to test.

Uses of Kernel in weka.filters.unsupervised.attribute

Methods in weka.filters.unsupervised.attribute that return Kernel

Modifier and Type	Method	Description
Kernel	KernelFilter.getKernel()	Gets the kernel to use.

Methods in weka.filters.unsupervised.attribute with parameters of type Kernel

Modifier and Type	Method	Description
void	KernelFilter.setKernel(Kernel value)	Sets the kernel to use.