By José L. Balcázar, Philip M. Long, Frank Stephan
This ebook constitutes the refereed court cases of the seventeenth foreign convention on Algorithmic studying thought, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the ninth foreign 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 laptop studying. They deal with subject matters corresponding to question versions, online studying, inductive inference, algorithmic forecasting, boosting, aid vector machines, kernel tools, reinforcement studying, and statistical studying models.
Read or Download Algorithmic Learning Theory: 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006. Proceedings PDF
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Extra resources for Algorithmic Learning Theory: 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006. Proceedings
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.
Even if DH cannot be eﬃciently evaluated, it might still be possible to compute some bits of information about g ab . In fact, it is known that the Legendre symbol of g ab is easy to compute from p, g, pa , pb . This bit of information is therefore “insecure”. In order to show that a particular bit of a cryptographic function G is “secure”, one typically proves that a general eﬃcient procedure, which reliably predicts this bit, can be converted into an eﬃcient procedure which evaluates G. Everybody who believes that the evaluation problem for G is intractable is therefore forced to believe that no such eﬃcient procedure for the particular bit is conceivable.
Avrim Blum, Merrick Furst, Jeﬀrey Jackson, Michael Kearns, Yishai Mansour, and Steven Rudich. Weakly learning DNF and characterizing statistical query learning using Fourier analysis. In Proceedings of the 26th Annual Symposium on Theory of Computing, pages 253–263, 1994. 3. Avrim Blum, Adam Kalai, and Hal Wasserman. Noise-tolerant learning, the parity problem, and the statistical query model. Journal of the Association on Computing Machinery, 50(4):506–519, 2003. 4. Dan Boneh and Ramarathnam Venkatesan.