Abstract
We introduce, discuss, and study a model for inductive inference from samplings, formalizing an idea of learning different “projections” of languages. One set of our results addresses the problem of finding a uniform learner for all samplings of a language from a certain set when learners for particular samplings are available. Another set of results deals with extending learnability from a large natural set of samplings to larger sets. A number of open problems is formulated.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Blum, L., Blum, M.: Toward a mathematical theory of inductive inference. Information and Control 28, 125–155 (1975)
Blum, M.: A machine-independent theory of the complexity of recursive functions. Journal of the ACM 14, 322–336 (1967)
Case, J., Lynes, C.: Machine inductive inference and language identification. In: Nielsen, M., Schmidt, E.M. (eds.) ICALP 1982. LNCS, vol. 140, pp. 107–115. Springer, Heidelberg (1982)
Case, J., Smith, C.: Comparison of identification criteria for machine inductive inference. Theoretical Computer Science 25, 193–220 (1983)
Freivalds, R.: Uniform and non-uniform predictability. In: Theory of Algorithms and Programs, vol. 1, pp. 89–100. Latvian State University, Riga (1974)
Fulk, M.: Prudence and other conditions on formal language learning. Information and Computation 85, 1–11 (1990)
Gold, E.M.: Language identification in the limit. Information and Control 10, 447–474 (1967)
Hopcroft, J., Ullman, J.: Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, Reading (1979)
Jain, S., Kinber, E.: Learning and extending sublanguages. Theoretical Computer Science A 397(1-3), 233–246 (2008); Special Issue on Forty Years of Inductive Inference. Dedicated to the 60th Birthday of Rolf Wiehagen
Osherson, D., Stob, M., Weinstein, S.: Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge (1986)
Osherson, D., Weinstein, S.: Criteria of language learning. Information and Control 52, 123–138 (1982)
Rogers, H.: Theory of Recursive Functions and Effective Computability. McGraw-Hill, New York (1967); Reprinted by MIT Press in 1987
Trakhtenbrot, B.A.: Frequency computations. In: Proceedings of Steklov Mathematical Institute, vol. 133, pp. 221–232. Academy of Sciences of USSR (1973) (in Russian)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jain, S., Kinber, E. (2010). Inductive Inference of Languages from Samplings. In: Hutter, M., Stephan, F., Vovk, V., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2010. Lecture Notes in Computer Science(), vol 6331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16108-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-16108-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16107-0
Online ISBN: 978-3-642-16108-7
eBook Packages: Computer ScienceComputer Science (R0)