document: Information theoretic regularization for semi-supervised boosting We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labeled and unlabeled training data . Our approach is based on extending information regularization framework to boosting , bearing loss functions that combine log loss on labeled data with the information-theoretic measures to encode unlabeled data . Even though the information-theoretic regularization terms make the optimization non-convex , we propose simple sequential gradient descent optimization algorithms , and obtain impressively improved results on synthetic , benchmark and real world tasks over supervised boosting algorithms which use the labeled data alone and a state-of-the-art semi-supervised boosting algorithm .
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