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

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

This booklet constitutes the refereed lawsuits of the 14th overseas convention on Algorithmic studying conception, 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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Additional info for Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings

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For a formal definition of uniform learning it is necessary to agree on a scheme for describing classes of recursive functions (i. e. describing learning problems). For that purpose we fix a three-place acceptable numbering ϕ. If d ∈ N, the numbering ϕd is the function resulting from ϕ, if the first input is fixed by d. Then any number d corresponds to a two-place numbering ϕd enumerating the set Pϕd of partial-recursive functions. Now it is conceivable to consider the subset of all total functions in Pϕd as a learning problem which is uniquely determined by the number d.

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28. S. Muggleton and L. De Raedt, Inductive logic programming: Theory and methods, Journal of Logic Programming, 19/20:669–679, 1994. 29. P. Nix, Editing by examples, Yale University, Dept. Computer Science, Technical Report 280, 1983. 30. N. Osherson, M. Stob and S. Weinstein, “Systems that Learn, An Introduction to Learning Theory for Cognitive and Computer Scientists,” MIT-Press, Cambridge, Massachusetts, 1986. 31. L. Pitt, Inductive Inference, DFAs and Computational Complexity, in “Proc. 2nd Int.

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