Robust classification using mixtures of dependency networks

Proceedings of the 4th European Workshop on Probabilistic Graphical Models, PGM 2008

Volumn , Issue , 2008, Pages 129-136

  Gámez, José A ^a   Mateo, Juan L ^a   Nielsen, Thomas D ^b   Puerta, José M ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

^b AALBORG UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC-LEARNING; CLASSIFICATION MODELS; DEPENDENCY NETWORKS; FAST ALGORITHMS; LEARNING TIME; MIXTURE COMPONENTS; PROBABILISTIC INFERENCE; ROBUST CLASSIFICATION;

BAYESIAN NETWORKS;

MIXTURES;

EID: 84874685844     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. D. Aha and D. Kibler. 1991. Instance-based learning algorithms. Machine Learning, 6:37-66.
2. A. Asuncion and D.J. Newman. 2007. UCI machine learning repository.
3. C.M. Bishop. 1995. Neural Networks for Pattern Recognition. Oxford University Press.
4. D.M. Chickering. 2002. Optimal Structure Identification With Greedy Search. Journal of Machine Learning Research, 3:507-554.
5. G.F. Cooper and E. Herskovits. 1992. A Bayesian Method for the Induction of Probabilistic Networks from data. Machine Learning, 9(4):309-347.
6. Elvira Consortium. 2002. Elvira: An Environment for Creating and Using Probabilistic Graphical Models. In Proceedings of the Fist European Workshop on Probabilistic Graphical Models, pages 222-230.
7. N. Friedman, D. Geiger, and M. Goldszmidt. 1997. Bayesian Network Classifiers. Machine Learning, 29(2-3):131-163.
8. J.A. Ǵamez, J.L. Mateo, and J.M. Puerta. 2006. Dependency networks based classifiers: learning models by using independence test. In Third European Workshop on Probabilistica Graphical Models (PGM06), pages 115-122.
9. J.A. Ǵamez, J.L. Mateo, and J.M. Puerta. 2008a. Improved EDNA (Estimation of Dependency Networks Algorithm) Using Combining Function with Bivariate Probability Distributions. In Genetic and Evolutionary Computation Conference (Gecco08).
10. J.A. Ǵamez, J.L. Mateo, and J.M. Puerta. 2008b. Towards consistency in general dependency networks. Technical Report DIAB-08-04-1, Computing Systems Deparment, University of Castilla-La Mancha.
11. D. Geiger and D. Heckerman. 1996. Knowledge representation and inference in similarity networks and bayesian multinets. Artificial Intelligence, 82:45-74.
12. S. Geman and D. Geman. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:147-156.
13. D. Heckerman, D.M. Chickering, C. Meek, R. Roun-thwaite, and C. Kadie. 2000. Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, 1:49-75.
14. P. Langley, W. Iba, and K. Thompson. 1992. An Analisys of bayesian Classifiers. In Proceedings of the 10th National Conference on Artificial Intelligence, pages 223-228.
15. J.M. Peña, R. Nilsson, J. Bj̈orkegren, and J. Tegńer. 2007. Towards scalable and data efficient learning of markov boundaries. International Journal of Approximate Reasoning, 45(2):211-232.
16. J. Platt, 1999. Advances in Kernel Methods-Support Vector Learning, chapter Fast Training of Support Vector Machines using Sequential Minimal Optimization, pages 185-208. MIT Press.
17. R. Quinlan. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann.
18. M. Sahami. 1996. Learning Limited Dependence Bayesian Classifiers. In Second International Conference on Knowledge Discovery in Databases, pages 335-338.
19. G. Schwarz. 1978. Estimating the dimension of a model. Annals of Statistics, 6(2):461-464.
20. P. Spirtes, C.N. Glymour, and R. Scheines. 2001. Causation, Prediction, and Search. MIT Press.
21. I. Tsamardinos, C.F. Aliferis, and A. Statnikov. 2003. Algorithms for large scale markov blanket discovery. In Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference FLAIRS 2003.
22. I.H. Witten and E. Frank. 2005. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2nd edition edition.