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
收藏
页码：707 / +
页数：2
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