On the relative sizes of learnable sets

被引：7

作者：

Fortnow, L

Freivalds, R

Gasarch, WI ^{[1
]}

Kummer, M

Kurtz, SA

Smith, CH

Stephan, F

机构：

[1] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA

[2] Univ Chicago, Dept Comp Sci, Chicago, IL 60637 USA

[3] Latvian State Univ, Inst Math & Comp Sci, LV-1459 Riga, Latvia

[4] Univ Karlsruhe, Inst Log Komplexitat & Dedukt Syst, D-76128 Karlsruhe, Germany

[5] Univ Heidelberg, Math Inst, D-69120 Heidelberg, Germany

来源：

THEORETICAL COMPUTER SCIENCE | 1998年 / 197卷 / 1-2期

基金：

美国国家科学基金会;

关键词：

inductive inference; measure; category;

D O I：

10.1016/S0304-3975(97)00175-8

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Measure and category (or rather, their recursion-theoretical counterparts) have been used in theoretical computer science to make precise the intuitive notion "for most of the recursive sets", We use the notions of effective measure and category to discuss the relative sizes of inferrible sets, and their complements. We find that inferable sets become large rather quickly in the standard hierarchies of learnability. On the other hand, the complements of the learnable sets are all large. (C) 1998-Elsevier Science B.V. All rights reserved.

引用

页码：139 / 156

页数：18

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