Learning recursive languages from good examples

被引：8
|
作者：
Lange, S
Nessel, J
Wiehagen, R
机构：
[1] HTWK Leipzig, FB Informat, D-04251 Leipzig, Germany
[2] Univ Kaiserslautern, Fachbereich Informat, D-67653 Kaiserslautern, Germany
来源：
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE | 1998年 / 23卷 / 1-2期
关键词：
D O I：
10.1023/A:1018955906119
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We study learning of indexable families of recursive languages from good examples. We show that this approach can be considerably more powerful than learning from all examples and point out reasons for this additional power. We present several characterizations of types of learning from good examples. We derive similarities as well as differences to learning of recursive functions from good examples.
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页码：27 / 52
页数：26
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