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

Eiji Takimoto, Manfred K. Warmuth

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

1 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルAlgorithmic Learning Theory - 10th International Conference, ALT 1999, Proceedings
編集者Osamu Watanabe, Takashi Yokomori
出版社Springer Verlag
ページ335-346
ページ数12
ISBN(印刷版)3540667482, 9783540667483
DOI
出版ステータス出版済み - 1999
外部発表はい
イベント10th International Conference on Algorithmic Learning Theory, ALT 1999 - Tokyo, 日本
継続期間: 12月 6 199912月 8 1999

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
1720
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他10th International Conference on Algorithmic Learning Theory, ALT 1999
国/地域日本
CityTokyo
Period12/6/9912/8/99

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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