Mutual information gaining algorithm and its relation to PAC-learning algorithm

Eiji Takimoto, Ichiro Tajika, Akira Maruoka

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

3 被引用数 (Scopus)

抄録

In this paper, the mutual information between a target concept and a hypothesis is used to measure the goodness of the hypothesis rather than the accuracy, and a notion of mutual information gaining (Mi-gaining) algorithms is introduced. In particular, strong and weak Mi-gaining algorithms are defined depending on the amount of information acquired, and their relation to strong and weak PAC-learning algorithms are investigated. It is shown that although a strong Mi-gaining algorithm is equivalent to a strong PAC-learning algorithm, a weak MI- gaining algorithm does not necessarily imply a weak PAC-learning algorithm, and vice versa. Moreover, a general boosting scheme for weak Mi-gaining algorithms is given. That is, any weak Mi-gaining algorithm can be used to build a strong one. Since a strong Mi-gaining algorithm is also a strong PAC-learning algorithm, the result can be viewed to give a sufficient condition for a class of algorithms to be boosted into strong learning algorithms.

本文言語英語
ホスト出版物のタイトルAlgorithmic Learning Theory - 4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994, Proceedings
編集者Setsuo Arikawa, Klaus P. Jantke
出版社Springer Verlag
ページ547-559
ページ数13
ISBN(印刷版)9783540585206
出版ステータス出版済み - 1 1 1994
外部発表はい
イベント4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994 - Reinhardsbrunn Castle, ドイツ
継続期間: 10 10 199410 15 1994

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

その他

その他4th International Workshop on Analogical and Inductive Inference, AII 1994 and 5th International Workshop on Algorithmic Learning Theory, ALT 1994
国/地域ドイツ
CityReinhardsbrunn Castle
Period10/10/9410/15/94

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)

フィンガープリント

「Mutual information gaining algorithm and its relation to PAC-learning algorithm」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル