Package weka.classifiers.functions.supportVector

Class Summary

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) 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 : K(x, y) = exp(-gamma*(x-y)^2) Valid options are:
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.