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

This e-book constitutes the refereed lawsuits of the fifteenth overseas convention on Algorithmic studying concept, ALT 2004, held in Padova, Italy in October 2004.The 29 revised complete papers provided 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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Extra info for Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings

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Now associate to each proof the value Probabilistic Inductive Logic Programming 29 where the product ranges over all clauses and denotes the number of times clause has been used in proof For stochastic context free grammars, the values correspond to the probabilities of the derivations. However, the difference between context free grammars and logic programs is that in grammars two rules of the form and always ‘resolve’ to give whereas resolution may fail due to unification. Therefore, the probability of a proof tree in The probability of a ground atom is then defined as the sum of all the probabilities of all the proofs for that ground atom.

Furthermore, Bayesian logic programs have already been employed for learning. The idea underlying Bayesian logic programs is to view ground atoms as random variables that are defined by the underlying definite clause programs. Furthermore, two types of predicates are distinguished: deterministic and probabilistic ones. The former are called logical, the latter Bayesian. Likewise we will also speak of Bayesian and logical atoms. A Bayesian logic program now consists of a set of of Bayesian (definite) clauses, which are expressions of the form where A is a Bayesian atom, are Bayesian and logical atoms and all variables are (implicitly) universally quantified.

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. Submitted for publication, 2004. J. Novikoff. On convergence proofs of Perceptrons. In Proceedings of the Symposium on the Mathematical Theory of Automata, volume XII, pages 615–622.

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