Package weka.classifiers.functions.supportVector

package weka.classifiers.functions.supportVector
  • Classes
    Base 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)^p
    This kernel is based on a static kernel matrix that is read from a file.
    The Pearson VII function-based universal kernel.

    For more information see:

    The RBF kernel : K(x, y) = exp(-gamma*(x-y)^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 :

    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 (SSK-LP) as described in [2].

    For more information, see

    Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.