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

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

This e-book constitutes the refereed complaints of the seventeenth overseas convention on Algorithmic studying thought, 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 desktop studying. They deal with subject matters equivalent 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 resources for Algorithmic Learning Theory: 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006. Proceedings

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L(Fn ) is polynomially bounded in n. We close this section by listing some rather surprising connections between SQ learning and (seemingly) different questions in learning and complexity theory, respectively: Corollary 8. There is a weak polynomial SQ learner for F = (Fn )n≥1 under the uniform distribution if at least one of the following conditions is satisfied: – There exists a poly(n)-dimensional half-space embedding for Fn . – There exists a half-space embedding for Fn that achieves a margin whose inverse is polynomially bounded in n.

7 Taken from [11] and used in connection with half-space embeddings in [6]. U. Simon A Note on the SQ Sampling Model: A query in the SQ Sampling model has the same form as a query in the CQ model but is answered by a τ -approximation for E[g(x)|f (x) = 1]. In the SQ sampling model, the learner pursues the goal to find a positive example for the unknown target concept. Blum and Yang [18] showed that the technique of Yang from [16, 17] leads to lower bounds in the SQ sampling model (when properly applied).

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.

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