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.
