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.
The goal of the CSPoker project is to develop Poker software and experiment with Artificial Intelligence for Poker. A Texas Hold'Em server written in Java and client software in JavaFX have already been developed. In the long run, it should be possible to train A.I. bots by playing against human players.