Inference of regular grammars via skeletons
IEEE Transactions on Systems, Man and Cybernetics
Grammatical interface for even linear languages based on control sets
Information Processing Letters
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Learning deterministic even linear languages from positive examples
Theoretical Computer Science - Special issue on algorithmic learning theory
Inference of Reversible Languages
Journal of the ACM (JACM)
On inferring zero-reversible languages
Acta Cybernetica
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Learning a Subclass of Context-Free Languages
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Learning code regular and code linear languages
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Identification of Function Distinguishable Languages
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Inferring Subclasses of Contextual Languages
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
A Characterization of Even Linear Languages and its Application to the Learning Problem
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Counter-Free Automata (M.I.T. research monograph no. 65)
Counter-Free Automata (M.I.T. research monograph no. 65)
Constructing an even grammar from a 2-element sample
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Learning Context-Sensitive Languages from Linear Structural Information
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Protein motif prediction by grammatical inference
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
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Learning from positive data is a center goal in grammatical inference. Some language classes have been characterized in order to allow its learning from text. There are two different approaches to this topic: (i) reducing the new classes to well known ones, and (ii) designing new learning algorithms for the new classes. In this work we will use reduction techniques to define new classes of even linear languages which can be inferred from positive data only. We will center our attention to inferable classes based on local testability features. So, the learning processes for such classes of even linear languages can be performed by using algorithms for locally testable regular languages.