Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). Applying weka.classifiers.functions.SMO to solve multiple instances problem.
The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.
For more information see:
Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003.
BibTeX:
@inproceedings{Andrews2003,
author = {Stuart Andrews and Ioannis Tsochantaridis and Thomas Hofmann},
booktitle = {Advances in Neural Information Processing Systems 15},
pages = {561-568},
publisher = {MIT Press},
title = {Support Vector Machines for Multiple-Instance Learning},
year = {2003}
}
Valid options are:
-D
If set, classifier is run in debug mode and
may output additional info to the console
-C <double>
The complexity constant C. (default 1)
-N <default 0>
Whether to 0=normalize/1=standardize/2=neither.
(default: 0=normalize)
-I <num>
The maximum number of iterations to perform.
(default: 500)
-K <classname and parameters>
The Kernel to use.
(default: weka.classifiers.functions.supportVector.PolyKernel)
Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
-D
Enables debugging output (if available) to be printed.
(default: off)
-no-checks
Turns off all checks - use with caution!
(default: checks on)
-C <num>
The size of the cache (a prime number).
(default: 250007)
-E <num>
The Exponent to use.
(default: 1.0)
-L
Use lower-order terms.
(default: no)
author:
Lin Dong (ld21@cs.waikato.ac.nz)
version:
$Revision: 1.4 $
See Also: weka.classifiers.functions.SMO | 