Smooth boosting using an information-based criterion

Kohei Hatano

doi:10.1007/11894841_25

Smooth boosting using an information-based criterion

Kohei Hatano

Intelligence Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Algorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings
Publisher	Springer Verlag
Pages	304-318
Number of pages	15
ISBN (Print)	3540466495, 9783540466499
DOIs	https://doi.org/10.1007/11894841_25
Publication status	Published - 2006
Event	17th International Conference on Algorithmic Learning Theory, ALT 2006 - Barcelona, Spain Duration: Oct 7 2006 → Oct 10 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4264 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	17th International Conference on Algorithmic Learning Theory, ALT 2006
Country/Territory	Spain
City	Barcelona
Period	10/7/06 → 10/10/06

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11894841_25

Cite this

Hatano, K. (2006). Smooth boosting using an information-based criterion. In Algorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings (pp. 304-318). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4264 LNAI). Springer Verlag. https://doi.org/10.1007/11894841_25

Smooth boosting using an information-based criterion. / Hatano, Kohei.
Algorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings. Springer Verlag, 2006. p. 304-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4264 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hatano, K 2006, Smooth boosting using an information-based criterion. in Algorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4264 LNAI, Springer Verlag, pp. 304-318, 17th International Conference on Algorithmic Learning Theory, ALT 2006, Barcelona, Spain, 10/7/06. https://doi.org/10.1007/11894841_25

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Smooth boosting using an information-based criterion

Abstract

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All Science Journal Classification (ASJC) codes

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