A multi-objective approach to RBF network learning

Neurocomputing

Volumn 71, Issue 7-9, 2008, Pages 1203-1209

  Kokshenev, Illya ^a   Braga, Antonio Padua ^a  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Complexity; Generalization; Multi objective learning; Radial basis functions; Regularization

Indexed keywords

COMPUTATIONAL COMPLEXITY; RADIAL BASIS FUNCTION NETWORKS; SUPERVISED LEARNING;

NETWORK SIZE REDUCTION;

MULTIOBJECTIVE OPTIMIZATION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; LEARNING STYLE; LEARNING THEORY; MACHINE LEARNING; MATHEMATICAL COMPUTING; NETWORK LEARNING; PRIORITY JOURNAL; PROCESS CONTROL; PROCESS OPTIMIZATION; RADIAL BASED FUNCTION; SUPPORT VECTOR MACHINE;

EID: 40849084986     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.11.021     Document Type: Article

References (21)

1. Adams R.A., and Fournier J.J. Sobolev Spaces. second ed. (2003), Academic press, New York
2. A. Asuncion, D. Newman, UCI machine learning repository. URL 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉, 2007.
3. Bartlett P.L. For valid generalization the size of the weights is more important than the size of the network. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds). Advances in Neural Information Processing Systems vol. 9 (1997), The MIT Press, Cambridge, MA 134
4. Braga A.P., Takahashi R.H.C., Costa M.A., and Teixeira R.A. Multi-objective algorithms for neural-networks learning. In: Jin Y. (Ed). Multi-Objective Machine Learning vol. 16 (2006), Springer, Heidelberg 151-171 (Series: Studies in Computational Intelligence)
5. M.A. Costa, A.P. Braga, Optimization of neural networks with multi-objective lasso algorithm, in: International Joint Conference on Neural Networks, Vancouver, 2006, pp. 6344-6350.
6. Costa M.A., Braga A., Menezes B.R., Teixeira R.A., and Parma G.G. Training neural networks with a multi-objective sliding mode control algorithm. Neurocomputing 51 (2003) 467-473
7. B. Efron, T. Hastie, I. Johnstone, R. Tibshirani, Least angle regression, Technical Report, Department of Statistics, Stanford University, 2002.
8. Geman S., Bienenstock E., and Doursat R. Neural network and the bias/variance dilemma. Neural Comput. 4 (1992) 1-58
9. Girosi F., Jones M., and Poggio T. Regularization theory and neural networks architectures. Neural Comput. 7 2 (1995) 219-269
10. In: Jin Y. (Ed). Multi-objective machine learning vol. 16 (2006), Springer, Heidelberg (Series: Studies in Computational Intelligence)
11. Jin Y., Okabe T., and Sendhoff B. Neural network regularization and ensembling using multi-objective evolutionary algorithms. Evol. Comput. CEC2004 1 (2004) 1-8
12. Liu G., Yang J., and Whidborne J. Multiobjective Optimisation and Control (2003), Research Studies Press, Baldock, Hertfordshire, England
13. MacQueen J.B. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (1967), University of California Press, Berkeley 281-297
14. L. Prechelt, Proben1: a set of neural network benchmark problems and benchmarking rules, Technical Report 21/94. URL 〈http://citeseer.ist.psu.edu/prechelt94proben.html〉, 1994.
15. Reed R. Pruning algorithms-a survey. IEEE Trans. Neural Networks 4 5 (1993) 740-746
16. Teixeira R.A., Braga A.P., Takahashi R.H.C., and Saldanha R.R. Improving generalization of MLPs with multi-objective optimization. Neurocomputing 35 (2000) 189-194
17. Tibshirani R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58 1 (1996) 267-288
18. Tikhonov A.N. Solution of incorrectly formulated problems and the regularization method. Sov. Math. Dokl. 4 (1963) 1035-1038
19. Tikhonov A.N., and Arsenin V.Y. Solutions of Ill-Posed Problems (1977), Wiley, New York
20. Vapnik V.N. Statistical Learning Theory (1998), Wiley, New York
21. Vapnik V.N., and Chervonenkis A.Y. Theory of Pattern Recognition (1974), Nauka, Moscow (in Russian)