Designing committees of models through deliberate weighting of data points

Journal of Machine Learning Research

Volumn 4, Issue 1, 2004, Pages 39-66

  Christensen, Stefan W ^a   Sinclair, Ian ^a   Reed, Philippa A S ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Bagging; Committees; Ensembles; Neural Networks

Indexed keywords

DECOMPOSITION; MATRIX ALGEBRA; NEURAL NETWORKS; OPTIMIZATION;

BAGGING; COMMITTEE DESIGN MATRIX; ENSEMBLES;

LEARNING SYSTEMS;

EID: 2342453339     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: 10.1162/153244304322765630     Document Type: Article

References (21)

1. L. Breiman. Bagging predictors. Machine Learning 24 (2): 123-140, 1996.
2. L. Breiman. Randomizing outputs to increase prediction accuracy. Technical Report No. 518. Department of Statistics, UC Berkeley, 1998.
3. L. Breiman. Combining predictors. In Amanda J. C. Sharkey, editor, Combining Artificial Neural Nets. Ensemble and Modular Multi-Net Systems, Perspectives in Neural Computing, 31-50. Springer, 1999.
4. L. Breiman & J. H. Friedman. Estimating optimal transformations for multiple regression and correlation. Journal of the American Statistical Association, 80, 580-619, 1985.
5. T. G. Dietterich. Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, Multiple Classier Systems, vol. 1857 of Lecture Notes in Computer Science, pages 1-15, Cagliari, Italy, Springer, 2000.
6. J. H. Friedman. Multivariate adaptive regression splines. The Annals of Statistics, 19 (1), 1-141, 1991.
7. L. K. Hansen, & P. Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12. No. 10, 993-1000, 1990.
8. S. Hashem. Optimal linear combinations of neural networks. Neural Networks, vol. 10 (4), 599-614, 1997.
9. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptive mixtures of local experts. Neural Computation 3, 79-87, 1991.
10. A. Krogh, & J. Vedelsby. Neural network ensembles, cross validation, and active learning. In G. Tesauro, D. S. Touretzky and T. K. Leen, eds. Advances in neural information processing systems 7, 231-238, MIT Press, Cambridge MA, 1995.
11. Y. Liao, & J. Moody. Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting. In Advances in Neural Information Processing Systems 12, S. A. Solla, T. K. Leen, K.-R. Mller, eds., MIT Press, 2000.
12. Y. Liu. & X. Yao. A Cooperative Ensemble Learning System. In Proc. of the 1998 IEEE International Joint Conference on Neural Networks (IJCNN'98), pp.2202-2207, Anchorage, USA, 1998.
13. Y. Liu, & X. Yao. Ensemble learning via negative correlation. Neural Networks 12, 1399-1404, 1999.
14. C. J. Mertz. Classification and regression by combining models. Ph.D. thesis, UC Irvine, 1998.
15. P. M. Murphy & D. W. Aha. UCI Repository of machine learning databases. University of California, Department of Information and Computer Science. Irvine, CA (1994).
16. D. W. Opitz, & J. W. Shavlik. Actively searching for an effective neural-network ensemble. Connection Sci., 8 (3-4), 1996.
17. M. P. Perrone, & L. N. Cooper. When networks disagree: Ensemble methods for hybrid neural networks. In R. J. Mammone, editor, Neural Networks for Speech and Image Processing, 1993.
18. Y. Raviv, & N. Intrator. Bootstrapping with noise: An effective regularization technique. Connection Science, Special issue on combining estimators, 8:356-372, 1996.
19. B. E. Rosen. Ensemble Learning using Decorrelated Neural Networks. Connection Science, Vol. 8, No. 3-4, pp. 373-384, 1996.
20. H. Schwefel. Numerical Optimization of Computer Models. Wiley, New York, 1981.
21. R. Shapire. The strength of weak learnability. Machine Learning, 5 (2), 197-227, 1990.