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

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

This publication constitutes the refereed lawsuits of the fifteenth overseas convention on Algorithmic studying idea, 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 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

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A complete survey of learning probabilistic logic representations can be found in [9]. 1 Parameter Estimation The problem of parameter estimation is thus concerned with estimating the values of the parameters of a fixed probabilistic logic program L that best explain the examples E. So, is a set of parameters and can be represented as a vector. e. though other scores or variants could – in principle – be used as well. When all examples are fully observable, maximum likelihood reduces to frequency counting.

Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5, pages 153–163. Edinburgh University Press, 1970. [40] D. Poole. Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64(1):81–129, 1993. [41] J. Quinlan and R. Cameron-Jones. Induction of logic programs: FOIL and related systems. New Generation Computing, 13(3–4):287–312, 1995. [42] T. Sato. A Statistical Learning Method for Logic Programs with Distribution Semantics. In L. Sterling, editor, Proceedings of the Twelfth International Conference on Logic Programming (ICLP-1995), pages 715 – 729, Tokyo, Japan, 1995.

M. Mitchell. Machine Learning. , 1997. [30] S. Muggleton. Inverse Entailment and Progol. New Generation Computing Journal, 13(3–4):245–286, 1995. [31] S. Muggleton. Stochastic logic programs. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 254–264. IOS Press, 1996. [32] S. Muggleton. Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 4(041), 2000. [33] S. Muggleton. Learning structure and parameters of stochastic logic programs. In S. Matwin and C.

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