On approximately identifying concept classes in the limit

Satoshi Kobayashi, Takashi Yokomori

研究成果: Conference contribution

24 被引用数 (Scopus)

抄録

In this paper, we introduce various kinds of approximations of a concept and propose a framework of approximate learning in case that a target concept could be outside the hypothesis space. We present some char­acterization theorems for approximately identifiability. In particular, we show a remarkable result that the upper-best approximate identifiability from com­plete data is collapsed into the upper-best approximate identifiability from positive data. Further, some other characterizations for approximate identifi­ability from positive data are presented, where we establish a relationship be­tween approximate identifiability and some important notions in quasi-order theory and topology theory. The results obtained in this paper are essentially related to the closure property of concept classes under infinite intersections (or infinite unions). We also show that there exist some interesting example concept classes with such properties (including specialized EFS’s) by which an upper-best approximation of any concept can be identifiable in the limit from positive data.

本文言語English
ホスト出版物のタイトルAlgorithmic Learning Theory - 6th International Workshop, ALT 1995, Proceedings
編集者Klaus P. Jantke, Takeshi Shinohara, Thomas Zeugmann
出版社Springer Verlag
ページ298-312
ページ数15
ISBN(印刷版)3540604545, 9783540604549
DOI
出版ステータスPublished - 1995
外部発表はい
イベント6th International Workshop on Algorithmic Learning Theory, ALT 1995 - Fukuoka, Japan
継続期間: 1995 10 181995 10 20

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)
997
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other6th International Workshop on Algorithmic Learning Theory, ALT 1995
国/地域Japan
CityFukuoka
Period95/10/1895/10/20

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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