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

Eiji Takimoto, Manfred K. Warmuth

研究成果: ジャーナルへの寄稿会議記事査読

16 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
ページ(範囲)217-235
ページ数19
ジャーナルTheoretical Computer Science
288
2
DOI
出版ステータス出版済み - 10月 16 2002
外部発表はい
イベントAlgorithmic Learning Theory (ALT 1999) - Tokyo, 日本
継続期間: 12月 6 199912月 8 1999

!!!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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