Grammatical Inference: Algorithms and Applications: 7th International Colloquium, ICGI 2004, Athens, Greece, October 11-13, 2004. Proceedings
Georgios Paliouras · Yasubumi Sakakibara
Jan 2005 · Springer
Ebook
296
Pages
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About this ebook
The 7th International Colloquium on Grammatical Inference (ICGI 2004) was held in the National Centre for Scientific Research "Demokritos", Athens, Greece on October 11-13, 2004. ICGI 2004 was the seventh in a series of successful biennial international conferences in the area of grammatical inference. Previous meetings were held in Essex, UK; Alicante, Spain; Montpellier, France; Ames, Iowa, USA; Lisbon, Portugal; and Amsterdam, The Netherlands. This series of conferences seeks to provide a forum for the presentation and discussion of original research papers on all aspects of grammatical inference. Grammatical inference, the study of learning grammars from data, is an established research field in artificial intelligence, dating back to the 1960s, and has been extensively addressed by researchers in automata theory, language acquisition, computational linguistics, machine learning, pattern recognition, computational learning theory and neural networks. ICGI 2004 emphasized the multidisciplinary nature of the research field and the diverse domains in which grammatical inference is being applied, such as natural language acquisition, computational biology, structural pattern recognition, information retrieval, Web mining, text processing, data compression and adaptive intelligent agents. We received 45 high-quality papers from 19 countries. The papers were - viewed by at least two - in most cases three - reviewers. In addition to the 20 full papers, 8 short papers that received positive comments from the reviewers were accepted, and they appear in a separate section of this volume. The topics of the accepted papers vary from theoretical results of learning algorithms to innovative applications of grammatical inference, and from learning several interesting classes of formal grammars to estimations of probabilistic grammars
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