Dynamic bayesian combination of multiple imperfect classifiers

Studies in Computational Intelligence

Volumn 474, Issue , 2013, Pages 1-35

  Simpson, Edwin ^a   Roberts, Stephen ^a   Psorakis, Ioannis ^a   Smith, Arfon ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b Zooniverse   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84893070992     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-36406-8_1     Document Type: Article

References (33)

1. Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, p. 1. ACM, New York (2004), http://doi.acm.org/10. 1145/1015330.1015430
2. Abramowitz, M., Stegun, I.A. (eds.): Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables. Dover Publications (1965)
3. Attias, H.: A Variational Bayesian Framework for Graphical Models. In: Advances in Neural Information Processing Systems 12, pp. 209-215 (2000)
4. Bierman, G.J.: Fixed interval smoothing with discrete measurements. International Journal of Control 18(1), 65-75 (1973), http://www.tandfonline. com/doi/abs/10.1080/00207177308932487
5. Bishop, C.M.: Pattern recognition and machine learning, 4th edn. Information Science and Statistics. Springer Science+Business Media, LLC (2006)
6. Bloodgood, M., Callison-Burch, C.: Using Mechanical Turk to build machine translation evaluation sets. In: Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazons Mechanical Turk, Los Angeles, pp. 208-211 (2010)
7. Choudrey, R., Roberts, S.: Variational mixture of Bayesian independent component analysers. Neural Computation 15(1) (2003)
8. Dash, R.K., Jennings, N.R., Parkes, D.C.: Computational-mechanism design: a call to arms. IEEE Intelligent Systems 18(6), 40-47 (2003)
9. Dawid, A.P., Skene, A.M.: Maximum likelihood estimation of observer Error-Rates using the EM algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28(1), 20-28 (1979), http://www.jstor.org/stable/ 2346806
10. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1-38 (1977)
11. Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861-874 (2006), http://dx.doi.org/10.1016/j.patrec.2005.10.010, doi:10.1016/j.patrec.2005.10.010
12. Fox, C., Roberts, S.: A tutorial on variational Bayesian inference. Artificial Intelligence Review 38(2), 85-95 (2011), http://www.springerlink.com/ content/j5617xn756j27671/abstract/, doi:10.1007/s10462-011-9236-8
13. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6(6), 721-741 (1984)
14. Ghahramani, Z., Kim, H.C.: Bayesian classifier combination. Gatsby Computational Neuroscience Unit Technical Report GCNU-T, London, UK (2003)
15. Gilks, W.R., Wild, P.: Adaptive rejection sampling for gibbs sampling. Journal of the Royal Statistical Society. Series C (Applied Statistics) 41(2), 337-348 (1992)
16. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821-7826 (2002), http://www.pnas.org/content/99/12/7821, doi:10.1073/pnas.122653799
17. Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79-86 (1951), http://www.jstor.org/stable/ 2236703
18. Law, N.M., Kulkarni, S.R., Dekany, R.G., Ofek, E.O., Quimby, R.M., Nugent, P.E., Surace, J., Grillmair, C.C., Bloom, J.S., Kasliwal, M.M., et al.: The Palomar transient factory: System overview, performance, and first results. Publications of the Astronomical Society of the Pacific 121(886), 1395-1408 (2009)
19. Lee, S.M., Roberts, S.J.: Sequential dynamic classification using latent variable models. The Computer Journal 53(9), 1415-1429 (2010), http://comjnl.oxfordjournals.org/content/53/9/1415, doi:10.1093/comjnl/bxp127
20. Lefkimmiatis, S., Maragos, P., Papandreou, G.: Bayesian inference on multiscale models for Poisson intensity estimation: Applications to Photon-Limited image denoising. IEEE Transactions on Image Processing 18(8), 1724-1741 (2009), doi:10.1109/TIP.2009.2022008
21. Littlestone, N.,Warmuth, M.K.: The weighted majority algorithm. In: 30th Annual Symposium on Foundations of Computer Science 1989, pp. 256-261 (1989)
22. Monteith, K., Carroll, J.L., Seppi, K., Martinez, T.: Turning Bayesian model averaging into Bayesian model combination. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2657-2663. IEEE (2011), doi:10.1109/IJCNN.2011.6033566
23. Parisi, G., Shankar, R.: Statistical field theory. Physics Today 41, 110 (1988)
24. Penny, W.D., Roberts, S.J.: Dynamic logistic regression. In: International Joint Conference on Neural Networks, IJCNN 1999, vol. 3, pp. 1562-1567 (2002)
25. Psorakis, I., Roberts, S.J., Ebden, M., Sheldon, B.: Overlapping community detection using Bayesian non-negative matrix factorization. Physical Review E 83(6) (2011), http://www.orchid.ac.uk/eprints/9/, doi:10.1103/PhysRevE. 83.066114
26. Quinn, A.J., Bederson, B.B., Yeh, T., Lin, J.: Crowdflow: Integrating machine learning with Mechanical Turk for speed-cost-quality flexibility. Tech. rep., HCIL-2010-09, University of Maryland, College Park (2010)
27. Raykar, V.C.,Yu, S., Zhao, L.H.,Valadez, G.H., Florin, C., Bogoni, L., Moy, L.: Learning from crowds. J. Mach. Learn. Res. 11, 1297-1322 (2010), http://dl.acm.org/citation.cfm?id=1756006.1859894
28. Smith, A.M., Lynn, S., Sullivan, M., Lintott, C.J., Nugent, P.E., Botyanszki, J., Kasliwal, M., Quimby, R., Bamford, S.P., Fortson, L.F., et al.: Galaxy Zoo Supernovae. Monthly Notices of the Royal Astronomical Society (2010)
29. Tan, V.Y.F., Fvotte, C.: Automatic relevance determination in nonnegative matrix factorization. In: SPARS 2009 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes-Bretagne Atlantique, Saint Malo, Royaume-Uni (2009), http://hal.inria.fr/view by stamp.php?&halsid= oerba5b5pkfqikvfb5e1ideks4&label=SPARS09&langue= en&action todo=view&id=inria-00369376&version=1
30. Tulyakov, S., Jaeger, S., Govindaraju, V., Doermann, D.: Review of classifier combinationmethods. In: Machine Learning in Document Analysis and Recognition, pp. 361-386 (2008)
31. Weatherburn, C.E.: A First Course in Mathematical Statistics. CUP Archive (1949)
32. West, M., Harrison, J.: Bayesian forecasting and dynamic models. Springer (1997)
33. West, M., Harrison, P.J., Migon, H.S.: Dynamic generalized linear models and Bayesian forecasting. Journal of the American Statistical Association 80(389), 73-83 (1985), http://www.jstor.org/stable/2288042, doi:10.2307/2288042