Package

weka.classifiers.functions

Synopsis

SVMreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm is selected by setting the RegOptimizer. The most popular algorithm (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default RegOptimizer.

For more information see:

S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999.

A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.

Options

The table below describes the options available for SVMreg.

Option	Description
c	The complexity parameter C.
debug	If set to true, classifier may output additional info to the console.
filterType	Determines how/if the data will be transformed.
kernel	The kernel to use.
regOptimizer	The learning algorithm.

Capabilities

The table below describes the capabilites of SVMreg.

Capability	Supported
Class	Date class, Numeric class, Missing class values
Attributes	Unary attributes, Nominal attributes, Numeric attributes, Missing values, Empty nominal attributes, Binary attributes
Min # of instances	1

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