Abstract
We shall establish the existence of a hierarchy of language families, in which the learning problem for each family is reduced to the learning problem for regular languages. Thus this boosts the learnability of learning algorithms for regular languages.
Preview
Unable to display preview. Download preview PDF.
References
Dana Angluin. Learning regular sets from queries and counter-examples. Information and Computation, 75:87–106, 1987.
V. Amar and G. Putzolu. On a family of linear grammars. Information and Control, 7:283–291, 1964.
Nabil A. Khabbaz. A geometric hierarchy of languages. Journal of Computer and System Sciences, 8:142–157, 1974.
José Oncina and Pedro GarcÃa. Identifying regular languages in polynomial time. In H. Bunke, editor, Advances in structural and syntactic pattern recognition, pages 99–108. World Scientific, Singapore, 1992.
V. Radhakrishnan and G. Nagaraja. Inference of even linear grammars and its application to picture description languages. Pattern Recognition, 21(1):55–62, 1988.
Arto Salomaa. Formal Languages. Academic Press, New York, 1973.
Yuji Takada. Grammatical inference for even linear languages based on control sets. Information Processing Letters, 28(4):193–199, 1988.
Yuji Takada. Algorithmic Learning Theory of Formal Languages and its Applications. Doctoral thesis, Hokkaido University, 1992. Also published as IIAS Research Report RR-93-6E.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takada, Y. (1994). A hierarchy of language families learnable by regular language learners. In: Carrasco, R.C., Oncina, J. (eds) Grammatical Inference and Applications. ICGI 1994. Lecture Notes in Computer Science, vol 862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58473-0_133
Download citation
DOI: https://doi.org/10.1007/3-540-58473-0_133
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58473-5
Online ISBN: 978-3-540-48985-6
eBook Packages: Springer Book Archive