Pattern recognition with a Bayesian kernel combination machine

Pattern Recognition Letters

Volumn 30, Issue 1, 2009, Pages 46-54

  Damoulas, Theodoros ^a   Girolami, Mark A ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Bayesian inference; Classification; Information integration; Kernel combination; MCMC; Probit regression

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; CLUSTER ANALYSIS; FEATURE EXTRACTION; INFERENCE ENGINES; LEARNING SYSTEMS; MARKOV PROCESSES; PATTERN RECOGNITION; RANDOM VARIABLES;

BAYESIAN INFERENCE; CLASSIFICATION; INFORMATION INTEGRATION; KERNEL COMBINATION; MCMC; PROBIT REGRESSION;

BAYESIAN NETWORKS;

EID: 55349088563     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.08.016     Document Type: Article

References (31)

1. Albert J., and Chib S. Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. 88 (1993) 669-679
2. Bellman R. Adaptive Control Processes: A Guided Tour (1961), Princeton University Press
3. Berger J.O. Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics (1985), Springer
4. Bishop C.M. Pattern Recognition and Machine Learning (2006), Springer, New York, USA
5. Damoulas T., and Girolami M.A. Probabilistic multi-class multi-kernel learning: On protein fold recognition and remote homology detection. Bioinformatics 24 10 (2008) 1264-1270
6. Denison D.G.T., Holmes C.C., Mallick B.K., and Smith A.F.M. Bayesian Methods for Nonlinear Classification and Regression. Wiley Series in Probability and Statistics (2002), West Sussex, UK
7. Dietterich, T.G., 2000. Ensemble methods in machine learning. In: Proceedings of the First International Workshop on Multiple Classifier Systems, pp. 1-15.
8. Gelman A., carlin J.B., Stern H.S., and Rubin D.B. Bayesian Data Analysis (2004), Chapman & Hall/CRC
9. Girolami, M., Rogers, S., 2005. Hierarchic Bayesian models for kernel learning. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 241-248.
10. Girolami, M., Zhong, M., 2007. Data integration for classification problems employing Gaussian process priors. In: Twentieth Annual Conference on Neural Information Processing Systems.
11. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning. Springer Series in Statistics (2001), Springer
12. Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
13. Holmes C.C., and Held L. Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Anal. 1 (2006) 145-168
14. Jaynes E.T. Probability Theory: The Logic of Science (2003), Cambridge University Press
15. Kittler J., Hatef M., Duin R.P.W., and Matas J. On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. 20 3 (1998) 226-239
16. Lanckriet G.R.G. Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 5 (2004) 27-72
17. Lee, W.-J., Verzakov, S., Duin, R.P., 2007. Kernel combination versus classifier combination. In: Seventh International Workshop on Multiple Classifier Systems.
18. Lewis, D. P., Jebara, T., Noble, W.S., 2006a. Nonstationary kernel combination. In: The 23rd International Conference on Machine Learning.
19. Lewis D.P., Jebara T., and Noble W.S. Support vector machine learning from heterogenous data: An empirical analysis using protein sequence and structure. Bioinformatics 22 22 (2006) 2753-2760
20. Neal R.M. Bayesian Learning for Neural Networks (1996), Springer-Verlag
21. Neal R.M. Regression and classification using Gaussian process priors. Bayesian Statist. 6 (1998) 475-501
22. Newman, D., Hettich, S., Blake, C., Merz, C., 1998. UCI Repository of Machine Learning Databases. .
23. Ong C.S., Smola A.J., and Williamson R.C. Learning the kernel with hyperkernels. J. Mach. Learn. Res. 6 (2005) 1043-1071
24. Pavlidis, P., Weston, J., Cai, J., Grundy, N.W., 2001. Gene functional classification from heterogenous data. In: Fifth Annual International Conference on Computational Molecular Biology, pp. 242-248.
25. Rao N.S.V. On fusers that perform better than best sensor. IEEE Trans. Pattern Anal. Machine Intell. 23 8 (2001) 904-909
26. Rao, N.S.V., 2004. A generic sensor fusion problem: Classification and function estimation. In: Multiple Classifier Systems.
27. Scho{combining double acute accent}lkopf B., and Smola A. Learning with Kernels (2002), The MIT Press
28. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge University Press
29. Tax D.M.J., van Breukelen M., Duin R.P.W., and Kittler J. Combining multiple classifiers by averaging or by multiplying?. Pattern Recogn. 33 (2000) 1475-1485
30. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1 (2001) 211-244
31. Wolpert D.H. Stacked generalization. Neural Networks 5 2 (1992) 241-259