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  • Produktbild: Grammatical Inference: Learning Syntax from Sentences
  • Produktbild: Grammatical Inference: Learning Syntax from Sentences
Band 1147

Grammatical Inference: Learning Syntax from Sentences Third International Colloquium, ICGI-96, Montpellier, France, September 25 - 27, 1996. Proceedings

Aus der Reihe Lecture Notes in Artificial Intelligence

Fr. 72.90

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.09.1996

Abbildungen

X, 334 p.

Herausgeber

Laurent Miclet + weitere

Verlag

Springer Berlin

Seitenzahl

334

Maße (L/B/H)

23.5/15.5/1.9 cm

Gewicht

522 g

Auflage

1996

Sprache

Englisch

ISBN

978-3-540-61778-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

16.09.1996

Abbildungen

X, 334 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

334

Maße (L/B/H)

23.5/15.5/1.9 cm

Gewicht

522 g

Auflage

1996

Sprache

Englisch

ISBN

978-3-540-61778-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Grammatical Inference: Learning Syntax from Sentences
  • Produktbild: Grammatical Inference: Learning Syntax from Sentences
  • Learning grammatical structure using statistical decision-trees.- Inductive inference from positive data: from heuristic to characterizing methods.- Unions of identifiable families of languages.- Characteristic sets for polynomial grammatical inference.- Query learning of subsequential transducers.- Lexical categorization: Fitting template grammars by incremental MDL optimization.- Selection criteria for word trigger pairs in language modeling.- Clustering of sequences using a minimum grammar complexity criterion.- A note on grammatical inference of slender context-free languages.- Learning linear grammars from structural information.- Learning of context-sensitive language acceptors through regular inference and constraint induction.- Inducing constraint grammars.- Introducing statistical dependencies and structural constraints in variable-length sequence models.- A disagreement count scheme for inference of constrained Markov networks.- Using knowledge to improve N-Gram language modelling through the MGGI methodology.- Discrete sequence prediction with commented Markov models.- Learning k-piecewise testable languages from positive data.- Learning code regular and code linear languages.- Incremental regular inference.- An incremental interactive algorithm for regular grammar inference.- Inductive logic programming for discrete event systems.- Stochastic simple recurrent neural networks.- Inferring stochastic regular grammars with recurrent neural networks.- Maximum mutual information and conditional maximum likelihood estimations of stochastic regular syntax-directed translation schemes.- Grammatical inference using Tabu Search.- Using domain information during the learning of a subsequential transducer.- Identification of DFA: Data-dependent versus data-independent algorithms.