Dynamically weighted majority voting for incremental learning and comparison of three boosting based approaches

Proceedings of the International Joint Conference on Neural Networks

Volumn 2, Issue , 2005, Pages 1131-1136

  Gangardiwala, Aliasgar ^a   Polikar, Robi ^a  

^a Rowan University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFIERS; DATA ACQUISITION; IMMUNIZATION; INPUT OUTPUT PROGRAMS; PLASTICITY; STABILITY;

ADABOOST; CATASTROPHIC FORGETTING; INCREMENTAL LEARNING ALGORITHM;

LEARNING SYSTEMS;

EID: 33745950074     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1556012     Document Type: Conference Paper

References (17)

1. S. Grossberg, "Nonlinear neural networks: principles, mechanisms and architectures," Neural Networks, vol.1, no. 1, pp. 17-61, 1988.
2. M. McCloskey and N. Cohen, "Catastrophic interference in connectionist networks: the sequential learning problem," in The Psychology of Learning and Motivation, G.H. Bower, ed., vol. 24, pp. 109-164, Academic Press, San Diego, 1989.
3. R. French, "Catastrophic forgetting in connectionist networks," Trends in Cognitive Sciences, vol. 3, no.4, pp. 128-135, 1999.
4. R. Polikar, L. Udpa L, S. Udpa, and V. Honavar, "Learn++: An incremental learning algorithm for supervised neural networks," IEEE Trans. on System, Man and Cybernetics (C), vol. 31, no. 4, pp. 497-508, 2001.
5. R. Polikar, J. Byorick, S. Krause, A. Marino, and M. Moreton, "Learn++: A classifier independent incremental learning algorithm for superv. neural netw.," Proc. of Int. Joint Conf. on Neural Networks., vol. 2, pp. 1742-1747, Honolulu, HI, 2002.
6. R. Polikar, L. Udpa L, S. Udpa, and V. Honavar, "An incremental learning algorithm with confidence estimation for automated identification of NDE signals," IEEE Trans. Ultraso., Ferro., Freq. Cont., vol. 51, no. 8, pp. 990-1001, 2004.
7. L. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. on PAMI, vol. 12, no. 10, pp. 993-1001, 1990.
8. Y. Freund and R. Schapire, "A decision theoretic generalization of online learning and an appli. to boosting," Comp. and System Sciences, vol. 57, no. 1, pp. 119-139, 1997.
9. N. Littlestone and M. Warmuth, "Weighted majority algorithm," Infor. and Comput., vol. 108, pp. 212-261, 1994.
10. M.I. Jordan and R.A. Jacobs, "Hierarchical mixtures of experts and the EM algorithm," Neural Computation, vol. 6, no. 2, pp. 181-214, 1994.
11. J. Kittler, M. Hatef, R.P. Duin, J. Matas, "On combining classifiers," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no.3, pp. 226-239, 1998.
12. L. Breiman, "Combining predictors," Combining Artificial Neural Nets, A.Sharkey, ed., pp. 31-50, NY: Springer 1999.
13. st Int. Wkshop on Mult. Class. Sys., LNCS, J. Killer, F. Roli, ed., vol. 1857, pp. 1-15, NY, Springer. 2000.
14. J. Ghosh, "Multiclassifier systems: back to the future," 3rd Int. Work. on Mult. Classifier Sys., LNCS (J. Kitler & F. Roli, eds), vol. 2364, p. 1-15, NY: Springer, 2002.
15. T. Windeatt and F. Roli (eds), In Proc. 4th Int. Workshop on MCS, LNCS, vol. 2709, NY, Springer, 2003.
16. W. H. Wolberg and O.L. Mangasarian, "Multisurface method of pattern separation for medical diagnosis applied to breast cytology," Proc. of the National Academy of Sciences, vol. 87, pp 9193-9196, 1990.
17. C.L. Blake and C.J. Merz, Univ. of California, Irvine, Repository of Machine Learning Databases at Irvine, CA.