String Extension Learning

被引：0

作者：

Heinz, Jeffrey ^{[1
]}

机构：

[1] Univ Delaware, Newark, DE 19716 USA

来源：

ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS | 2010年

关键词：

LANGUAGES; IDENTIFICATION; FAMILIES; WORD;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper provides a unified, learning-theoretic analysis of several learnable classes of languages discussed previously in the literature. The analysis shows that for these classes an incremental, globally consistent, locally conservative, set-driven learner always exists. Additionally, the analysis provides a recipe for constructing new learnable classes. Potential applications include learnable models for aspects of natural language and cognition.

引用

页码：897 / 906

页数：10

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