Consistency conditions for inductive inference of recursive functions

Yohji Akama, Thomas Zeugmann

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A consistent learner is required to correctly and completely reflect in its actual hypothesis all data received so far. Though this demand sounds quite plausible, it may lead to the unsolvability of the learning problem. Therefore, in the present paper several variations of consistent learning are introduced and studied. These variations allow a so-called δ-delay relaxing the consistency demand to all but the last δ data. Additionally, we introduce the notion of coherent learning (again with δ-delay) requiring the learner to correctly reflect only the last datum (only the n -δth datum) seen. Our results are threefold. First, it is shown that all models of coherent learning with δ-delay are exactly as powerful as their corresponding consistent learning models with δ-delay. Second, we provide characterizations for consistent learning with δ-delay in terms of complexity. Finally, we establish strict hierarchies for all consistent learning models with δ-delay in dependence on δ.

本文言語English
ホスト出版物のタイトルNew Frontiers in Artificial Intelligence - JSAI 2006 Conference and Workshops, Revised Selected Papers
出版社Springer Verlag
ページ251-264
ページ数14
ISBN(印刷版)3540699015, 9783540699019
DOI
出版ステータスPublished - 2007
イベント20th Annual Conference of the Japanese Society for Artificial Intelligence, JSAI 2006 Conference and Workshops - Tokyo, Japan
継続期間: 2006 6 52006 6 9

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)
4384 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other20th Annual Conference of the Japanese Society for Artificial Intelligence, JSAI 2006 Conference and Workshops
国/地域Japan
CityTokyo
Period06/6/506/6/9

ASJC Scopus subject areas

  • 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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