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Abstract: One way to represent a machine learning algorithm's bias over the hypothesis and instance space is as a pair of probability distributions. This approach has been taken both within Bayesian learning schemes and the framework of U-learnability. However, it is not obvious how an Inductive Logic Programming (ILP) system should best be provided with a probability distribution. This paper extends the results of a previous paper by the author which introduced stochastic logic programs as a means of... (Update)
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BibTeX entry: (Update)
Muggleton S., "Stochastic Logic Programs", Advances in Inductive Logic Programming (Ed. L. De Raedt), IOS Press (1996) 254--264 http://citeseer.ist.psu.edu/muggleton96stochastic.html More
@inproceedings{ muggleton95stochastic,
author = "Muggleton, S.",
title = "Stochastic Logic Programs",
booktitle = "Proceedings of the 5th International Workshop on Inductive Logic Programming",
publisher = "Department of Computer Science, Katholieke Universiteit Leuven",
editor = "De Raedt, L.",
pages = "29",
year = "1995",
url = "citeseer.ist.psu.edu/muggleton96stochastic.html" }
Citations (may not include all citations):1362 A tutorial on hidden markov models and selected applications.. (context) - Rabiner - 1989
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493 Communications of the ACM (context) - Valiant, of et al. - 1984
388 Inductive logic programming - Muggleton - 1991
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85 Bounds on the sample complexity of Bayesian learning using i.. - Haussler, Kearns et al. - 1991
79 Probabilistic logic programming - Ng, Subrahmanian - 1992
60 A Theory of Learning Classification Rules - Buntine - 1990
53 Stochastic logic programs - Muggleton - 1995
39 Learning from positive data (context) - Muggleton - 1996
23 Bayesian inductive logic programming - Muggleton - 1994
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22 A learnability model for universal representations - Muggleton, Page - 1994
18 The Laws of Thought (context) - Boole
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5 A generalisation of bayesian inference (context) - Dempster - 1968
1 Artificial Intelligence Journal (context) - Nilsson - 1986
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