Using prior probabilities and density estimation for relational classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1446, Issue , 1998, Pages 106-115

  Cussens, James ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COMPUTATIONAL LINGUISTICS; COMPUTER CIRCUITS;

BACK-GROUND KNOWLEDGE; BAYES THEOREM; BAYESIAN METHODS; DENSITY ESTIMATION; GIBBS SAMPLING; PART OF SPEECH TAGGING; POSITIVE-ONLY LEARNING; PRIOR PROBABILITY;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 27144440444     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0027314     Document Type: Conference Paper

References (13)

1. Henrik Bostrom. Predicate invention and learning from positive examples only. In Proceedings of the 10th European Conference on Machine Learning (ECML-98). Springer, 1998.
2. Wray Buntine. Learning classification trees. In D. J. Hand, editor, Artificial Intelligence Frontiers in Statistics: AI and Statistics III, chapter 15, pages 182-201. Chapman & Hall, London, 1993.
3. J. Cussens. Bayesian Inductive Logic Programming with explicit probabilistic bias. Technical Report PRG-TR-24-96, Oxford University Computing Laboratory, 1996.
4. James Cussens. Part-of-speech tagging using Progol. In Inductive Logic Programming: Proceedings of the 7th International Workshop (ILP-97). LNAI 1297, pages 93-108. Springer, 1997.
5. Luc Dehaspe. Maximum entropy modeling with clausal constraints. In Inductive Logic Programming: Proceedings of the 7th International Workshop (ILP-97). LNAI 1297, pages 109-124. Springer, 1997.
6. Joseph Y. Halpern. An analysis of first-order logics of probability. Artificial Intelligence, 46: 311-350, 1990.
7. D. Michie, D. J. Spiegelhalter, and C. C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, Hemel Hempstead, 1994.
8. S. Muggleton. Learning from positive data. In S. Muggleton, editor, Induetive Logic Programming: Proceedings of the 6th International Workshop (ILP-96). LNAI 1314, pages 358-376. Springer, 1996.
9. Raymond N. and V. S. Subrahmanian. Probabilistic logic programming. Information and Computation, 101(2): 150-201, 1992.
10. Uroff Pompe and Igor Kononenko. Probabilistic first-order classification. In Induetive Logic Programming: Proceedings of the 7th International Workshop (ILP-97), pages 235-243, 1997.
11. A. F. M. Smith and G. O. Roberts. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society B, 55(1): 3-23, 1993.
12. A. Srinivasan. Sampling methods for the analysis of large datasets with ILP. Technical Report PRG-TR-27-97, Oxford University Computing Laboratory, Oxford, 1997.
13. A. Srinivasan and R. D. King. Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. In S. Muggleton, editor, Inductive Logic Programming: Proceedings of the 6th International Workshop (ILP-96). LNAI 1314, pages 89-104. Springer, 1996.