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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	September 2000
Genre:	EDV / Informatik
	algorithmanalysisandproblemcomplexity / Algorithmen und Datenstrukturen / Algorithms / Automat / Automata / Compiler und Übersetzer / computationallinguistics / Intelligenz / Künstliche Intelligenz / KI / Künstliche Intelligenz - AI / Learning / Linguistics / machinelearning / Natural / Pattern (EDV) / patternrecognition / Spracherkennung / Syntax / Theoretische Informatik mehr...
ISBN:	9783540410119
EAN-Code:	9783540410119
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 18 mm
Gewicht:	493 gr
Seiten:	324
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Inference of Finite-State Transducers by Using Regular Grammars and Morphisms.- Computational Complexity of Problems on Probabilistic Grammars and Transducers.- Efficient Ambiguity Detection in C-NFA.- Learning Regular Languages Using Non Deterministic Finite Automata.- Smoothing Probabilistic Automata: An Error-Correcting Approach.- Inferring Subclasses of Contextual Languages.- Permutations and Control Sets for Learning Non-regular Language Families.- On the Complexity of Consistent Identification of Some Classes of Structure Languages.- Computation of Substring Probabilities in Stochastic Grammars.- A Comparative Study of Two Algorithms for Automata Identification.- The Induction of Temporal Grammatical Rules from Multivariate Time Series.- Identification in the Limit with Probability One of Stochastic Deterministic Finite Automata.- Iterated Transductions and Efficient Learning from Positive Data: A Unifying View.- An Inverse Limit of Context-Free Grammars - A New Approach to Identifiability in the Limit.- Synthesizing Context Free Grammars from Sample Strings Based on Inductive CYK Algorithm.- Combination of Estimation Algorithms and Grammatical Inference Techniques to Learn Stochastic Context-Free Grammars.- On the Relationship between Models for Learning in Helpful Environments.- Probabilistic k-Testable Tree Languages.- Learning Context-Free Grammars from Partially Structured Examples.- Identification of Tree Translation Rules from Examples.- Counting Extensional Differences in BC-Learning.- Constructive Learning of Context-Free Languages with a Subpansive Tree.- A Polynomial Time Learning Algorithm of Simple Deterministic Languages via Membership Queries and a Representative Sample.- Improve the Learning of Subsequential Transducers by Using Alignments andDictionaries.