Smooth boosting using an information-based criterion

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationAlgorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings
PublisherSpringer Verlag
Pages304-318
Number of pages15
ISBN (Print)3540466495, 9783540466499
DOIs
Publication statusPublished - 2006
Event17th International Conference on Algorithmic Learning Theory, ALT 2006 - Barcelona, Spain
Duration: Oct 7 2006Oct 10 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4264 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Algorithmic Learning Theory, ALT 2006
CountrySpain
CityBarcelona
Period10/7/0610/10/06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • 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