Package weka.classifiers.functions.supportVector

Class Summary
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)

Valid options are:

PolyKernel The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p

Valid options are:

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 SVMreg 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.