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- W1489772951 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." @default.
- W1489772951 created "2016-06-24" @default.
- W1489772951 creator A5018051479 @default.
- W1489772951 date "2006-01-01" @default.
- W1489772951 modified "2023-09-24" @default.
- W1489772951 title "Smooth Boosting Using an Information-Based Criterion" @default.
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- W1489772951 doi "https://doi.org/10.1007/11894841_25" @default.
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