Grammatical inference : learning syntax from sentences : Third International Colloquium, ICGI-96, Montpellier, France, September 25-27, 1996 : proceedings
This book constitutes the refereed proceedings of the Third International Colloquium on Grammatical Inference, ICGI-96, held in Montpellier, France, in September 1996. The 25 revised full papers contained in the book together with two invited key papers by Magerman and Knuutila were carefully select...
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| Materialtyp: | Livre numérique |
| Språk: | Anglais |
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Berlin [etc.] :
Springer
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Cham : Springer Nature |
| Serie: | Lecture notes in computer science. Lecture notes in artificial intelligence
1147 |
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| Länkar: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Grammatical inference, learning syntax from sentences, Third International Colloquium, ICGI-96, Montpellier, France, September 1996 proceedings, Laurent Miclet, Colin de la Higuera, eds, 1996, New York, Springer, 1 vol. (VIII-325 p.), Lecture notes in computer science, 3-540-61778-7 • Grammatical Inference: Learning Syntax from Sentences, Texte imprimé, 9783662200933 |
Innehållsförteckning:
- 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.