Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.
Comment: Migrated to Confluence 4.0

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