Grammatical Inference with Grammar-based Classifier System

被引：0

作者：

Unold, Olgierd ^{[1
]}

机构：

[1] Wroclaw Univ Technol, Inst Comp Engn Control & Robot, Wyb Wyspianskiego 27, PL-50370 Wroclaw, Poland

来源：

NEW ASPECTS OF SYSTEMS, PTS I AND II | 2008年

关键词：

Machine Learning; Grammatical Inference; Learning Classifier Systems; Natural Language Processing; Promoter Recognition;

D O I：

暂无

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

This paper takes up the topic of a task of training Grammar-based Classifier System (GCS) to learn grammar from data. GCS is a new model of Learning Classifier Systems in which the population of classifiers has a form of a context-free grammar rule set in a Chomsky Normal Form. GCS has been proposed to address both the natural language grammar induction and also learning formal grammar for DNA sequence. In both cases near-optimal solutions or better than reported in the literature were obtained.

引用

页码：707 / +

页数：2

共 37 条

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