Grammatical Inference with Grammar-based Classifier System
نویسنده
چکیده
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. Key-Words: Machine Learning, Grammatical Inference, Learning Classifier Systems, Natural Language Processing, Promoter Recognition
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Grammar-based Classifier System: A Universal Tool for Grammatical Inference
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