Noise model on learning sets of strings

Yasubumi Sakakibara, Rani Siromoney

研究成果: Conference contribution

15 被引用数 (Scopus)

抄録

In this paper, we introduce a new noise model on learning sets of strings in the framework of PAC learning and consider the effect of the noise on learning. The instance domain is the set Σn of strings over a finite alphabet Σ, and the examples are corrupted by purely random errors affecting only the instances (and not the labels). We consider three types of errors on instances, called EDIT operation errors. EDIT operations consist of `insertion', `deletion', and `change' of a symbol in a string. We call such a noise where the examples are corrupted by random errors of EDIT operations on instances the EDIT noise. First we show general upper bounds on the EDIT noise rate that a learning algorithm of taking the strategy of minimizing disagreements can tolerate and a learning algorithm can tolerate. Next we present an efficient algorithm that can learn a class of decision lists over the attributes `a string w contains a pattern p?' from noisy examples under some restriction on the EDIT noise rate.

本文言語English
ホスト出版物のタイトルProceedings of the Fifth Annual ACM Workshop on Computational Learning Theory
出版社Publ by ACM
ページ295-302
ページ数8
ISBN(印刷版)089791497X
出版ステータスPublished - 1992 12月 1
外部発表はい
イベントProceedings of the Fifth Annual ACM Workshop on Computational Learning Theory - Pittsburgh, PA, USA
継続期間: 1992 7月 271992 7月 29

出版物シリーズ

名前Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory

Other

OtherProceedings of the Fifth Annual ACM Workshop on Computational Learning Theory
CityPittsburgh, PA, USA
Period92/7/2792/7/29

ASJC Scopus subject areas

  • 工学(全般)

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