Package

weka.classifiers.functions

Synopsis

Implements Alex Smola and Bernhard Scholkopf's sequential minimal optimization algorithm for training a support vector regression model. This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes by default. (Note that the coefficients in the output are based on the normalized/standardized data, not the original data.)

For more information on the SMO algorithm, see

Alex J. Smola, Bernhard Schoelkopf: A Tutorial on Support Vector Regression. In NeuroCOLT2 Technical Report Series, 1998.

S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy (1999). Improvements to SMO Algorithm for SVM Regression. Control Division Dept of Mechanical and Production Engineering, National University of Singapore.

Options

The table below describes the options available for SMOreg.

Option	Description
c	The complexity parameter C.
checksTurnedOff	Turns time-consuming checks off - use with caution.
debug	If set to true, classifier may output additional info to the console.
eps	The epsilon for round-off error (shouldn't be changed).
epsilon	The amount up to which deviations are tolerated. Watch out, the value of epsilon is used with the (normalized/standardized) data.
filterType	Determines how/if the data will be transformed.
kernel	The kernel to use.
toleranceParameter	The tolerance parameter (shouldn't be changed).

Capabilities

The table below describes the capabilites of SMOreg.

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