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

This publication constitutes the refereed complaints of the seventeenth foreign convention on Algorithmic studying conception, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the ninth overseas convention on Discovery technological know-how, 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 computing device studying. They handle issues similar to question types, 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 NIPS 16, 2003. 4. J. Demiris and G. Hayes. A robot controller using learning by imitation, 1994. 5. Michael Kearns and Satinder Singh. Near-optimal reinforcement learning in polynomial time. Machine Learning journal, 2002. 6. Y. Kuniyoshi, M. Inaba, and H. Inoue. Learning by watching: Extracting reusable task knowledge from visual observation of human performance. T-RA, 10:799–822, 1994. 7. John Langford and Bianca Zadrozny. Relating reinforcement learning performance to classification performance.

R: O(1) Exi [|f˜i (xi )|] < Exi [|f˜i (xi ) − fi (xi )|] + Exi [|fi (xi )|] ≤ 1 + √ . k =1

Let H be a class of ±1-valued functions over [b]n and let f : [b]n → {−1, 1} be expressible as f = Majority(h1 , . . , hs ) where each hi ∈ H and h1 (x) + . . + hs (x) = 0 for all x. Then for any distribution D over [b]n there is some hi such that |ED [f hi ]| ≥ 1/s. 3 The Generalized Harmonic Sieve Algorithm In this section our goal is to describe a variant of Jackson’s Harmonic Sieve Algorithm and show that under suitable conditions it can efficiently learn certain functions f : [b]n → {−1, 1}.

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