On the sample complexity of consistent learning with one-sided error

Eiji Takimoto, Akira Maruoka

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Although consistent learning is sufficient for PAC-learning, it has not been found what strategy makes learning more efficient, especially on the sample complexity, i.e., the number of examples required. For the first step towards this problem, only classes that have consistent learning algorithms with one-sided error are considered. A combinatorial quantity called maximal particle sets is introduced, and an upper bound of the sample complexity of consistent learning with one-sided error is obtained in terms of maximal particle sets. For the class of n-dimensional parallel axis rectangles, one of those classes that are consistently learnable with one-sided error, the cardinality of the maximal particle set is estimated and (Formula Found) upper bound of the learning algorithm for the class is obtained. This bound improves the bounds due to Blumer et al. [2] and meets the lower bound within a constant factor.

本文言語英語
ホスト出版物のタイトルAlgorithmic Learning Theory - 4th International Workshop, ALT 1993, Proceedings
編集者Klaus P. Jantke, Shigenobu Kobayashi, Etsuji Tomita, Takashi Yokomori
出版社Springer Verlag
ページ265-278
ページ数14
ISBN(印刷版)9783540573708
DOI
出版ステータス出版済み - 1 1 1993
外部発表はい
イベント4th Workshop on Algorithmic Learning Theory, ALT 1993 - Tokyo, 日本
継続期間: 11 8 199311 10 1993

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

その他

その他4th Workshop on Algorithmic Learning Theory, ALT 1993
国/地域日本
CityTokyo
Period11/8/9311/10/93

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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