Download Algorithmic Learning Theory: 17th International Conference, by José L. Balcázar, Philip M. Long, Frank Stephan PDF

By José L. Balcázar, Philip M. Long, Frank Stephan

This publication constitutes the refereed lawsuits of the seventeenth overseas convention on Algorithmic studying idea, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the ninth foreign convention on Discovery technology, DS 2006.

The 24 revised complete papers awarded including the abstracts of 5 invited papers have been rigorously reviewed and chosen from fifty three submissions. The papers are devoted to the theoretical foundations of computer studying. They handle themes corresponding to question versions, online studying, inductive inference, algorithmic forecasting, boosting, help vector machines, kernel equipment, reinforcement studying, and statistical studying models.

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Additional info for Algorithmic Learning Theory: 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006. Proceedings

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In Proc. ICML, 2005. 8. A. Y. Ng and S. Russell. Algorithms for inverse reinforcement learning. In Proc. ICML, 2000. Learning Unions of ω(1)-Dimensional Rectangles Alp Atıcı and Rocco A. edu Abstract. We consider the problem of learning unions of rectangles over the domain [b]n , in the uniform distribution membership query learning setting, where both b and n are “large”. We obtain poly(n, log b)-time algorithms for the following classes: log b) )-dimensional rectangles. – poly(n log b)-Majority of O( loglog(n log(n log b) – Unions of poly(log(n log b)) many rectangles with dimension 2 (n log b) ).

G. [4, 6, 7, 10, 13, 19]), where a rectangle is a conjunction of properties of the form “the value of attribute xi lies in the range [αi , βi ]”. One motivation for studying these classes is that they are a natural analogue of classes of DNF (Disjunctive Normal Form) formulae over {0, 1}n ; for instance, it is easy to see that in the case b = 2 any union of s rectangles is simply a DNF with s terms. Since the description length of a point x ∈ [b]n is n log b bits, a natural goal in learning functions over [b]n is to obtain algorithms which run in time poly(n log b).

Following an idea from [4], the new algorithm works by identifying a subset of “sensitive” elements from [b] for each of the n dimensions. Definition 4 (See [4]). A value σ ∈ [b] is called i-sensitive with respect to f : [b]n → {−1, 1} if there exist values c1 , c2 , . . , ci−1 , ci+1 , . . , cn ∈ [b] such that f (c1 , . . , ci−1 , σ − 1, ci+1 , . . , cn ) = f (c1 , . . , ci−1 , σ, ci+1 , . . , cn ). A value σ is called sensitive with respect to f if σ is i-sensitive for some i. If there is no i-sensitive value with respect to f , we say index i is trivial.

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