Grammar-based Classifier System (GCS) is one of the evolutionary approached context-free grammar induction methods. Any learning process creates a large amount of data, which is hard to analyze in a raw form. In the present paper we aim to present a reporting tool, created to facilitate analysis and conclusion drawing by presenting learning data in a neat and readable form, yet fully conveying its complexity.
Abstract-Grammar-based Classifier System (GCS) is one of the evolutionary approached context-free grammar induction methods. Any learning process creates a large amount of data, which is hard to analyze in a raw form. In the present paper we aim to present a reporting tool, created to facilitate analysis and conclusion drawing by presenting learning data in a neat and readable form, yet fully conveying its complexity.
Grammatical inference is a machine learning area, whose fundamentals are built around learning sets. At present, real-life data and examples from manually crafted grammars are used to test their learning performance. This paper aims to present a method of generating artificial context-free grammars with their optimal learning sets, which could be successfully applied as a benchmarking tool for empirical grammar inference methods.
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