Grammatical Inference: Algorithms and Applications

Chapter 1 Inference of Finite-State Transducers by Using Regular Grammars and Morphisms

Chapter 2 Computational Complexity of Problems on Probabilistic Grammars and Transducers

Chapter 3 Efficient Ambiguity Detection in C -NFA

Chapter 4 Learning Regular Languages Using Non Deterministic Finite Automata

Chapter 5 Smoothing Probabilistic Automata: An Error-Correcting Approach

Chapter 6 Inferring Subclasses of Contextual Languages

Chapter 7 Permutations and Control Sets for Learning Non-regular Language Families

Chapter 8 On the Complexity of Consistent Identification of Some Classes of Structure Languages

Chapter 9 Computation of Substring Probabilities in Stochastic Grammars

Chapter 10 A Comparative Study of Two Algorithms for Automata Identification

Chapter 11 The Induction of Temporal Grammatical Rules from Multivariate Time Series

Chapter 12 Identification in the Limit with Probability One of Stochastic Deterministic Finite Automata

Chapter 13 Iterated Transductions and Efficient Learning from Positive Data: A Unifying View

Chapter 14 An Inverse Limit of Context-Free Grammars – A New Approach to Identifiability in the Limit

Chapter 15 Synthesizing Context Free Grammars from Sample Strings Based on Inductive CYK Algorithm

Chapter 16 Combination of Estimation Algorithms and Grammatical Inference Techniques to Learn Stochastic Context-Free Grammars

Chapter 17 On the Relationship between Models for Learning in Helpful Environments

Chapter 18 Probabilistic k -Testable Tree Languages

Chapter 19 Learning Context-Free Grammars from Partially Structured Examples

Chapter 20 Identification of Tree Translation Rules from Examples

Chapter 21 Counting Extensional Differences in BC-Learning

Chapter 22 Constructive Learning of Context-Free Languages with a Subpansive Tree 

Chapter 23 A Polynomial Time Learning Algorithm of Simple Deterministic Languages via Membership Queries and a Representative Sample

Chapter 24 Improve the Learning of Subsequential Transducers by Using Alignments and Dictionaries