Algorithmic Learning Theory: 15th International Conference, by David Sh. B. (Ed), Case J. (Ed), Maruoka A. (Ed)

By David Sh. B. (Ed), Case J. (Ed), Maruoka A. (Ed)

This booklet constitutes the refereed complaints of the fifteenth foreign convention on Algorithmic studying idea, ALT 2004, held in Padova, Italy in October 2004.The 29 revised complete papers awarded including five invited papers and three instructional summaries have been rigorously reviewed and chosen from ninety one submissions. The papers are geared up in topical sections on inductive inference, PAC studying and boosting, statistical supervised studying, on-line series studying, approximate optimization algorithms, good judgment established studying, and question and reinforcement studying.

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Additional resources for Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings

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Heckerman. A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research, March 1995. [17] N. Helft. Induction as nonmonotonic inference. In R. Brachman, H. Levesque, and R. Reiter, editors, Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning (KR-1989), pages 149–156, Toronto, Canada, May 15-18 1989. Morgan Kaufmann. [18] M. Jaeger. Relational Bayesian networks. In D. Geiger and P. Shenoy, editors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 266–273, Providence, Rhode Island, USA, 1997.

3] N. Cesa-Bianchi, A. Conconi, and C. Gentile. Learning probabilistic linearthreshold classifiers via selective sampling. In Proceedings of the 16th Annual Conference on Learning Theory, pages 373–386. LNAI 2777, Springer, 2003. [4] N. Cesa-Bianchi, A. Conconi, and C. Gentile. Regret bounds for hierarchical classification with linear-threshold functions. In Proceedings of the 17th Annual Conference on Learning Theory. Springer, 2004. [5] N. Cesa-Bianchi, C. Gentile, and L. Zaniboni. Worst-case analysis of selective sampling for linear-threshold algorithms.

In S. Matwin and C. Sammut, editors, Proceedings of the Twelfth International Conference on Inductive Logic Prgramming (ILP-02), volume 2583 of LNCS, pages 198–206, Sydney, Australia, 2002. Springer. [34] S. Muggleton and C. Feng. Efficient induction of logic programs. In S. Muggleton, editor, Inductive Logic Programming, pages 281–298. Acadamic Press, 1992. [35] L. Ngo and P. Haddawy. Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171(1–2):147–177, 1997.

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