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
IEEE Transactions on Pattern Analysis and Machine Intelligence
The grammatical inference problem for the Szilard languages of linear grammars
Information Processing Letters
A hierarchy of language families learnable by regular language learning
Information and Computation
A note on the grammatical inference problem for even linear languages
Fundamenta Informaticae
Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
Inference of Reversible Languages
Journal of the ACM (JACM)
Structural inference for semistructured data
Proceedings of the tenth international conference on Information and knowledge management
Even linear simple matrix languages: formal language properties and grammatical inference
Theoretical Computer Science
Predicate Invention and Learning from Positive Examples Only
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning a Subclass of Linear Languages from Positive Structural Information
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Permutations and Control Sets for Learning Non-regular Language Families
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
A Comparison of Syntactic and Statistical Techniques for Off-Line OCR
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Forming Grammars for Structured Documents: an Application of Grammatical Inference
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Algorithms for Learning Function Distinguishable Regular Languages
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Learning Even Equal Matrix Languages Based on Control Sets
ICPIA '92 Proceedings of the Second International Conference on Parallel Image Analysis
Learning Formal Languages Based on Control Sets
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Approximative Learning of Regular Languages
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Parameterized Complexity
Approximative Learning of Regular Languages
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Identification of Function Distinguishable Languages
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Identification of function distinguishable languages
Theoretical Computer Science
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We discuss new efficient learning algorithms for certain subclasses of regular and even linear languages based on the notion of terminal distinguishability introduced by Radhakrishnan and Nagaraja. The learning model we use is identification in the limit from positive samples as proposed by Gold and further studied by Angluin and many others. All classes we introduce in this paper are modifications of the language families TDRL (terminal distinguishable regular) and TDELL (terminal distinguishable even linear) defined by Radhakrishnan and Nagaraja. A tradeoff between the power of the language class and the time complexity of the identification algorithm is observed when the size of the underlying alphabet is considered as an additional parameter. Extending the classes of efficiently learnable languages is also important from the viewpoint of applications of the algorithms. One of these extensions is obtained basically by making use of the concept of control language which is known from formal language theory and has been employed for learning theoretic purposes in particular by Takada.