Smooth Boosting Using an Information-Based Criterion

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.

• Type: Conference
• Year: 2006
• Where: ALT
• Authors: Kohei Hatano
• Keywords: ALT 2006, Current Smooth Boosting, Machine Learning, Non-smooth Boosting Algorithms, Smooth Boosting Algorithms