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

The 19 revised complete papers offered including 2 invited papers and abstracts of three invited talks have been conscientiously reviewed and chosen from 37 submissions. The papers are prepared 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

Example text

First of all, our learner is fed with positive examples only, while in Muggleton’s [27] model examples labeled with respect to their containment in the target language are provided. Next, we do not make any assumption concerning the distribution of the target patterns. Furthermore, we do not measure the expected total learning time with respect to a given class of distributions over the targets and a given class of distributions for the sampling process, but exclusively in dependence on the length of the target.

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. Muggleton, Bayesian Inductive Logic Programming, in “Proc.

Fix a recursive meta-operator Θ and a recursive operator Ξ. D1 is rUEx -reducible to D2 via Θ and Ξ, iff for any d1 ∈ D1 , any f1 ∈ Rd1 , and any initial segment δ1 there are functions δ2 and f2 satisfying: 1. Θ(δ1 d∞ 1 , f1 ) = (δ2 , f2 ), 2. δ2 converges to some description d2 ∈ D2 such that f2 ∈ Rd2 , 3. if σ is r -admissible for d2 and f2 , then Ξ(d2 σ) is r -admissible for d1 and f1 . D1 is rUEx -reducible to D2 , iff D1 is rUEx -reducible to D2 via some Θ and Ξ . Completeness is defined as usual.

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