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).
BibTeX:
@article{Littlestone1988,
author = {N. Littlestone},
journal = {Machine Learning},
pages = {285-318},
title = {Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm},
volume = {2},
year = {1988}
}
@techreport{Littlestone1989,
address = {University of California, Santa Cruz},
author = {N. Littlestone},
institution = {University of California},
note = {Technical Report UCSC-CRL-89-11},
title = {Mistake bounds and logarithmic linear-threshold learning algorithms},
year = {1989}
}
Valid options are:
-L
Use the baLanced version
(default false)
-I <int>
The number of iterations to be performed.
(default 1)
-A <double>
Promotion coefficient alpha.
(default 2.0)
-B <double>
Demotion coefficient beta.
(default 0.5)
-H <double>
Prediction threshold.
(default -1.0 == number of attributes)
-W <double>
Starting weights.
(default 2.0)
-S <int>
Default random seed.
(default 1)
author:
J. Lindgren (jtlindgr at cs.helsinki.fi)
version:
$Revision: 1.12 $ | 