Grammatical Inference: Algorithms and Applications: 5th International Colloquium, ICGI 2000, Lisbon, Portugal, September 11-13, 2000 Proceedings: Lecture Notes in Computer Science, cartea 1891

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.

Includes supplementary material: sn.pub/extras