Grammatical Inference: 4th International Colloquium, ICGI-98, Ames, Iowa, USA, July 12-14, 1998, Proceedings

ISBN-13: 9783540647768 / Angielski / Miękka / 1998 / 277 str.

Vasant Honavar;Giora Slutzki

Opis

This book constitutes the refereed proceedings of the Fourth International Colloquium on Grammatical Inference, ICGI-98, held in Ames, Iowa, in July 1998.
The 23 revised full papers were carefully reviewed and selected for inclusion in the book from a total of 35 submissions. The book addresses a wide range of grammatical inference theory such as automata induction, grammar induction, automatic language acquisition, etc. as well as a variety of applications in areas like syntactic pattern recognition, adaptive intelligent agents, diagnosis, computational biology, data mining, and knowledge discovery.

Spis treści

Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm.- Learning k-variable pattern languages efficiently stochastically finite on average from positive data.- Meaning helps learning syntax.- A polynomial time incremental algorithm for learning DFA.- The data driven approach applied to the OSTIA algorithm.- Grammar model and grammar induction in the system NL PAGE.- Approximate learning of random subsequential transducers.- Learning stochastic finite automata from experts.- Learning a deterministic finite automaton with a recurrent neural network.- Applying grammatical inference in learning a language model for oral dialogue.- Real language learning.- A stochastic search approach to grammar induction.- Transducer-learning experiments on language understanding.- Locally threshold testable languages in strict sense: Application to the inference problem.- Learning a subclass of linear languages from positive structural information.- Grammatical inference in document recognition.- Stochastic inference of regular tree languages.- How considering incompatible state mergings may reduce the DFA induction search tree.- Learning regular grammars to model musical style: Comparing different coding schemes.- Learning a subclass of context-free languages.- Using symbol clustering to improve probabilistic automaton inference.- A performance evaluation of automatic survey classifiers.- Pattern discovery in biosequences.