On-line algorithm to predict nearly as well as the best pruning of a decision tree

Akira Maruoka, Eiji Takimoto

研究成果: Chapter in Book/Report/Conference proceedingChapter

抄録

We review underlying mechanisms of the multiplicative weight-update prediction algorithms which somehow combine experts' predictions to obtain its own prediction that is almost as good as the best expert's prediction. Looking into the mechanisms we show how such an algorithm with the experts arranged on one layer can be naturally generalized to the one with the experts laid on nodes of trees. Consequently we give an on-line prediction algorithm that, when given a decision tree, produces predictions not much worse than the predictions made by the best pruning of the given decision tree.

本文言語英語
ホスト出版物のタイトルProgress in Discovery Science
出版社Springer Verlag
ページ296-306
ページ数11
ISBN(印刷版)3540433384, 9783540433385
DOI
出版ステータス出版済み - 2002
外部発表はい

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)
2281
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

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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