weka.classifiers.functions.supportVector

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## 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.

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