Package

weka.classifiers.functions

Synopsis

Implements Winnow and Balanced Winnow algorithms by Littlestone.

For more information, see

N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318.

N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz.

Does classification for problems with nominal attributes (which it converts into binary attributes).

The table below describes the options available for Winnow.

Option	Description
alpha	Promotion coefficient alpha.
balanced	Whether to use the balanced version of the algorithm.
beta	Demotion coefficient beta.
debug	If set to true, classifier may output additional info to the console.
defaultWeight	Initial value of weights/coefficients.
numIterations	The number of iterations to be performed.
seed	Random number seed used for data shuffling (-1 means no randomization).
threshold	Prediction threshold (-1 means: set to number of attributes).

The table below describes the capabilites of Winnow.

Capability	Supported
Class	Binary class, Missing class values
Attributes	Unary attributes, Binary attributes, Nominal attributes, Empty nominal attributes, Missing values
Min # of instances	0