A bias-variance-complexity trade-off framework for complex system modeling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3980 LNCS, Issue , 2006, Pages 518-527

  Yu, Lean ^a,b   Lai, Kin Keung ^b,c   Wang, Shouyang ^a,c   Huang, Wei ^d  

^a INSTITUTE OF SYSTEMS SCIENCE   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^c HUNAN UNIVERSITY   (China)

^d HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BUDGET CONTROL; COMPUTATIONAL METHODS; LARGE SCALE SYSTEMS; MATHEMATICAL MODELS;

BIAS-VARIANCE-COMPLEXITY; FINANCIAL SYSTEM; SYSTEM MODELING;

COMPUTATIONAL COMPLEXITY;

EID: 33745951285     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11751540_55     Document Type: Conference Paper

References (13)

1. Geman, S., Bienenstock, E., Doursat, R.: Neural networks and the bias/variance dilemma. Neural Computation 4 (1992) 1-58
2. Myung, I. J., Pitt, M. A.: Applying Okham's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin & Review 4 (1996) 79-95
3. Moody, J.: Prediction risk and architecture selection for neural networks. In: Cherkassky, V., Friedman, J. H., Wechsler, H. (eds.): From Statistics to Neural Networks: Theory and Pattern Recognition Applications. NATO ASI Series F, Springer-Verlag, 1994
4. Hardle, W., Hall, P., Marron, J. S.: How far are automatically chosen regression smoothing parameters from their optimum? Journal of the American Statistical Association 83 (1988) 86-95
5. Akaike, H.: Statistical predictor information. Annals of the Institute of Statistical Mathematics 22 (1970) 203-217
6. Shwartz, G.: Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-464
7. Hansen, L. K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 993-1001
8. Opitz, D, Maclin, R.: Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 11 (1999) 169-198
9. Breiman, L.: Bagging predictors. Machine Learning 24 (1996) 123-140
10. Freund, Y., Schapire, R. E.: Experiments with a new boosting algorithm. Machine Learning: Proceedings of the 13th International Conference (1996) 148-156
11. Krogh, A., Sollich, P.: Statistical mechanics of ensemble learning. Physical Review E 55 (1997) 811-825
12. Perrone, M. P., Cooper, L. N.: When neural networks disagree: Ensemble methods for hybrid neural networks. In: Mammone, R. J. (ed.): Neural Networks for Speech and Image Processing. Chapman-Hall (1993) 126-142
13. Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Tesauro, G., Touretzky, D., Leen, D. (eds.): Advances in Neural Information Processing Systems. MIT Press (1995) 231-238