# Package weka.classifiers.functions.supportVector

package weka.classifiers.functions.supportVector

- Related Packages
  Package
  Description
  weka.classifiers.functions
  weka.classifiers.functions.neural
- 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 : 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.