Predicting nearly as well as the best pruning of a planar decision graph

Eiji Takimoto, Manfred K. Warmuth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We design efficient on-line algorithms that predict nearly as well as the best pruning of a planar decision graph. We assume that the graph has no cycles. As in the previous work on decision trees, we implicitly maintain one weight for each of the prunings (exponentially many). The method works for a large class of algorithms that update its weights multiplicatively. It can also be used to design algorithms that predict nearly as well as the best convex combination of prunings.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 10th International Conference, ALT 1999, Proceedings
EditorsOsamu Watanabe, Takashi Yokomori
PublisherSpringer Verlag
Pages335-346
Number of pages12
ISBN (Print)3540667482, 9783540667483
DOIs
Publication statusPublished - Jan 1 1999
Event10th International Conference on Algorithmic Learning Theory, ALT 1999 - Tokyo, Japan
Duration: Dec 6 1999Dec 8 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1720
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Algorithmic Learning Theory, ALT 1999
CountryJapan
CityTokyo
Period12/6/9912/8/99

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Takimoto, E., & Warmuth, M. K. (1999). Predicting nearly as well as the best pruning of a planar decision graph. In O. Watanabe, & T. Yokomori (Eds.), Algorithmic Learning Theory - 10th International Conference, ALT 1999, Proceedings (pp. 335-346). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1720). Springer Verlag. https://doi.org/10.1007/3-540-46769-6_28