Smooth Boosting Using an Information-Based Criterion

Author: Kohei Hatano

Source: Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, October 2006, Proceedings, (José L. Balcázar, Phil Long and Frank Stephan, Eds.), Lecture Notes in Artificial Intelligence 4264, pp. 304 - 318, Springer 2006.

Abstract. Smooth boosting algorithms are variants of boosting methods which handle only smooth distributions on the data. They are proved to be noise-tolerant and can be used in the “boosting by filtering” scheme, which is suitable for learning over huge data. However, current smooth boosting algorithms have rooms for improvements: Among non-smooth boosting algorithms, real AdaBoost or InfoBoost, can perform more efficiently than typical boosting algorithms by using an information-based criterion for choosing hypotheses. In this paper, we propose a new smooth boosting algorithm with another information-based criterion based on Gini index. we show that it inherits the advantages of two approaches, smooth boosting and information-based approaches.

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