New PDF release: Algorithmic Learning Theory: 14th International Conference,

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

ISBN-10: 3540202919

ISBN-13: 9783540202912

This publication constitutes the refereed complaints of the 14th overseas convention on Algorithmic studying concept, 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 geared up in topical sections on inductive inference, studying and data extraction, studying with queries, studying with non-linear optimization, studying from random examples, and on-line prediction.

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

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Of all infinite limiting r. e. sets such that ϕd0 ∈ C and ϕdx+1 =↑∞ for all i, x ∈ N and d ∈ Ai . Let A := i∈N Ai and C = {f0 , f1 , . . }. Define a set D0 as follows. Fix the least elements d0 , d0 of A0 , d0 < d0 . Let I0 := {d0 }, I0 := {d0 }. Let e0 ∈ A \ (I0 ∪ I0 ) be minimal such that f0 ∈ Re0 . (* e0 exists, because A contains infinitely many descriptions d with ϕd0 = f0 . *) Let D0 := I0 ∪ {e0 }. (* The disjoint sets D0 and I0 both intersect with A0 ; some recursive core described by D0 equals {f0 }.

Thus, the bound obtained is exponentially better than the bound provided within the PAC model. Our approach also differs from U-learnability introduced by Muggleton [27]. 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.

190–258, Springer-Verlag, Berlin, 1995. 52. T. Zeugmann, S. Lange and S. Kapur, Characterizations of monotonic and dual monotonic language learning, Information and Computation 120, 155–173, 1995. de Abstract. Inductive inference is concerned with algorithmic learning of recursive functions. In the model of learning in the limit a learner successful for a class of recursive functions must eventually find a program for any function in the class from a gradually growing sequence of its values. This approach is generalized in uniform learning, where the problem of synthesizing a successful learner for a class of functions from a description of this class is considered.

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Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings by Thomas Eiter (auth.), Ricard Gavaldá, Klaus P. Jantke, Eiji Takimoto (eds.)


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