By David Sh. B. (Ed), Case J. (Ed), Maruoka A. (Ed)
This publication constitutes the refereed court cases of the fifteenth overseas convention on Algorithmic studying idea, ALT 2004, held in Padova, Italy in October 2004.The 29 revised complete papers offered including five invited papers and three instructional summaries have been conscientiously reviewed and chosen from ninety one submissions. The papers are prepared in topical sections on inductive inference, PAC studying and boosting, statistical supervised studying, on-line series studying, approximate optimization algorithms, good judgment dependent 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
Separate-and-Conquer Rule Learning. Artificial Intelligence Review, 13(1):3–54, 1999.  L. Getoor and D. Jensen, editors. Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-03), 2003.  P. Haddawy. Generating Bayesian networks from probabilistic logic knowledge bases. In R. López de Mántaras and D. Poole, editors, Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-1994), pages 262–269, Seattle, Washington, USA, 1994.
Rouveirol and M. Sebag, editors, Proceedings of the Eleventh Conference on Inductive Logic Programming (ILP-01), volume 2157 of LNCS, Strasbourg, France, 2001. Springer.  K. Kersting and L. De Raedt. Bayesian logic programs. Technical Report 151, University of Freiburg, Institute for Computer Science, April 2001.  K. Kersting and L. De Raedt. Towards Combining Inductive Logic Programming and Bayesian Networks. In C. Rouveirol and M. Sebag, editors, Proceedings of the Eleventh Conference on Inductive Logic Programming (ILP-01), volume 2157 of LNCS, Strasbourg, France, 2001.
2 31 Structure Learning The problem is now to learn both the structure L and the parameters of the probabilistic logic program from data. Often, further information is given as well. It can take various different forms, including: 1. a language bias that imposes restrictions on the syntax of the definite clauses allowed in L, 2. a background theory, 3. an initial hypothesis from which the learning process can start, and 4. a scoring function that may correct the maximum likelihood principle for complex hypthesis; this can be based on a Bayesian approach which takes into account priors or the minimum description length princple.
Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings by David Sh. B. (Ed), Case J. (Ed), Maruoka A. (Ed)