Grammar-based Classifier System: A Universal Tool for Grammatical Inference
نویسنده
چکیده
Grammatical Inference deals with the problem of learning structural models, such as grammars, from different sort of data patterns, such as artificial languages, natural languages, biosequences, speech and so on. This article describes a new grammatical inference tool, Grammar-based Classifier System (GCS) dedicated 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 regular language induction and the natural language grammar induction as well as learning formal grammar for DNA sequence. In all cases near-optimal solutions or better than reported in the literature were obtained. Key-Words: Machine Learning, Grammatical Inference, Learning Classifier Systems, Regular Language Induction, DFA Induction, Natural Language Processing, Promoter Recognition
منابع مشابه
Grammatical Inference with Grammar-based Classifier System
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 ...
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