Algorithmic Learning Theory: 14th International Conference, by Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji

By Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji Takimoto (eds.)

This booklet constitutes the refereed lawsuits of the 14th foreign convention on Algorithmic studying thought, ALT 2003, held in Sapporo, Japan in October 2003.

The 19 revised complete papers provided including 2 invited papers and abstracts of three invited talks have been rigorously reviewed and chosen from 37 submissions. The papers are geared up in topical sections on inductive inference, studying and knowledge extraction, studying with queries, studying with non-linear optimization, studying from random examples, and on-line prediction.

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Extra info for Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings

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Mitchell. “Machine Learning,” McGraw Hill, 1997. 25. A. Mitchell, A. Sharma, T. Scheffer and F. Stephan, The VC-dimension of Subclasses of Pattern Languages, in “Proc. 10th International Conference on Algorithmic Learning Theory,” (O. Watanabe and T. ), Lecture Notes in Artificial Intelligence, Vol. 1720, pp. 93–105, Springer-Verlag, Berlin, 1999. 26. S. Miyano, A. Shinohara and T. Shinohara, Polynomial-time learning of elementary formal systems, New Generation Computing, 18:217–242, 2000. 27. S.

Then σ is admissible for ψj . This implies that Ξ(σ) is admissible for both χx and χx . As χx = χx , this is impossible. So ψi = ψj . Sufficiency. Assume D, ψ, and (di )i∈N fulfil the conditions of Theorem 7. Let d denote a numbering associated to the limiting r. e. family (di )i∈N . The results in the context of non-uniform learning help to show that D is UEx -complete: By assumption, Pψ is a dense r. e. subset of R. Theorem 1 then implies that Pψ is Ex -complete, so Cfsup is Ex -reducible to Pψ , say via Θ , Ξ .

4. D. H. Smith. Formal inductive inference. C. ), Vol. 1, pp. 409–418, Wiley-Interscience Publication, New York. 5. S. Arikawa, T. Shinohara and A. Yamamoto, Learning elementary formal systems, Theoretical Computer Science 95, 97–113, 1992. 6. L. Blum and M. Blum, Toward a mathematical theory of inductive inference, Information and Control 28, 125–155, 1975. 7. A. Blumer, A. Ehrenfeucht, D. Haussler and M. Warmuth, Learnability and the Vapnik-Chervonenkis Dimension, Journal of the ACM 36 (1989), 929–965.

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