# Package weka.classifiers.functions.supportVector

package weka.classifiers.functions.supportVector

Related Packages
Package
Description
weka.classifiers.functions
weka.classifiers.functions.neural
weka.classifiers.functions.pace
Classes
Class
Description
CachedKernel
Base class for RBFKernel and PolyKernel that implements a simple LRU.
CheckKernel
Class for examining the capabilities and finding problems with kernels.
Kernel
Abstract kernel.
KernelEvaluation
Class for evaluating Kernels.
NormalizedPolyKernel
The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
PolyKernel
The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
PrecomputedKernelMatrixKernel
This kernel is based on a static kernel matrix that is read from a file.
Puk
The Pearson VII function-based universal kernel.
For more information see:
B.
RBFKernel
The RBF kernel.
RegOptimizer
Base class implementation for learning algorithm of SMOreg Valid options are:
RegSMO
Implementation of SMO for support vector regression as described in :
A.J.
RegSMOImproved
Learn SVM for regression using SMO with Shevade, Keerthi, et al.
SMOset
Stores a set of integer of a given size.
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.