We provide an implementation of four SVM-based active learning algorithms:
"Simple", "Self Conf", "KFF", and "Balanced". See references (1) and
(2) for further information regarding these algorithms.
This implementation has two main components: Experimenter and Learner. The Experimenter outputs a learning curve graph (for the given algorithm) based on k-fold cross validation. The learner implements a standard active learner interface ("learn", "query" and "classify"). The base code is a Java 1.4.* code. We also provide a Matlab code (wrapper) for the learner component. All relevant parameters are fully configurable via a textual configuration file.
|Documentation||Matlab Readme Click to download pdf|
Java implementation. Click to download zip;
The code is written in J2SE
Matlab wrapper: read the following readme.pdf; download the wrapper files zip
Note: the current version is a Beta (ver. 0.1)
|Credit||Ron Begleiter provided the current implementation on top of Kobi Luz's code|
|This code is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License (GPL) for more details.|