Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Polynomial-time identification of very simple grammars from positive data
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
IEICE - Transactions on Information and Systems
Polynomial-time identification of an extension of very simple grammars from positive data
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
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This paper is concerned with a subclass of finite state transducers, called strict prefix deterministic finite state transducers (SPDFST's for short), and studies a problem of identifying the subclass in the limit from positive data. After providing some properties of languages accepted by SPDFST's, we showthat the class of SPDFST's is polynomial time identifiable in the limit from positive data in the sense of Yokomori.