icsiboost r102 (Default branch) |
|
|
Boosting is a meta-learning approach that aims at
combining an ensemble of weak classifiers to form
a strong classifier. Adaptive Boosting (Adaboost)
implements this idea as a greedy search for a
linear combination of classifiers by overweighting
the examples that are misclassified by each
classifier. icsiboost implements Adaboost over
stumps (one-level decision trees) on discrete and
continuous attributes (words and real values).
This approach is one of the most efficient and
simple to combine continuous and nominal values.
This implementation is aimed at allowing training
from millions of examples by hundreds of features
in a reasonable amount of time/memory.
License: GNU Lesser General Public License (LGPL)
Changes:
This release brings a few bugfixes in training and test procedures, and error rate reports on multi-class problems. Moreover, optimization of the most called functions brought nice training speed improvements. This release also updates the documentation and tries to improve the handling of rare cases. The F-measure framework has been widely tested on diverse classification problems.
|
