Package weka.classifiers.functions.supportVector
package weka.classifiers.functions.supportVector

ClassDescriptionBase class for RBFKernel and PolyKernel that implements a simple LRU.Class for examining the capabilities and finding problems with kernels.Abstract kernel.Class for evaluating Kernels.The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^pThis kernel is based on a static kernel matrix that is read from a file.The Pearson VII functionbased universal kernel.
For more information see:
B.The RBF kernel : K(x, y) = exp(gamma*(xy)^2)
Valid options are:Base class implementation for learning algorithm of SMOreg Valid options are:Implementation of SMO for support vector regression as described in :
A.J.Learn SVM for regression using SMO with Shevade, Keerthi, et al.Stores a set of integer of a given size.Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSKLP) as described in [2].
For more information, see
Huma Lodhi, Craig Saunders, John ShaweTaylor, Nello Cristianini, Christopher J.