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Vasant Honavar and Giora Slutzki
Front matter
1-12
Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm
13-24
Learning k-variable pattern languages efficiently stochastically finite on average from positive data
25-36
Meaning helps learning syntax
37-49
A polynomial time incremental algorithm for learning DFA
50-56
The data driven approach applied to the OSTIA algorithm
57-66
Grammar model and grammar induction in the system NL PAGE
67-78
Approximate learning of random subsequential transducers
79-89
Learning stochastic finite automata from experts
90-101
Learning a deterministic finite automaton with a recurrent neural network
102-113
Applying grammatical inference in learning a language model for oral dialogue
114-125
Real language learning
126-137
A stochastic search approach to grammar induction
138-149
Transducer-learning experiments on language understanding
150-161
Locally threshold testable languages in strict sense: Application to the inference problem
162-174
Learning a subclass of linear languages from positive structural information
175-186
Grammatical inference in document recognition
187-198
Stochastic inference of regular tree languages
199-210
How considering incompatible state mergings may reduce the DFA induction search tree
211-222
Learning regular grammars to model musical style: Comparing different coding schemes
223-231
Learning a subclass of context-free languages
232-243
Using symbol clustering to improve probabilistic automaton inference
244-256
A performance evaluation of automatic survey classifiers
257-270
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