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Optimization Techniques for Solving Complex Problems (Wiley by Enrique Alba, Christian Blum, Pedro Asasi, Coromoto Leon,

By Enrique Alba, Christian Blum, Pedro Asasi, Coromoto Leon, Juan Antonio Gomez

Fixing advanced difficulties addresses actual difficulties and the trendy optimization innovations used to unravel them. Thorough examples illustrate the purposes themselves, in addition to the particular functionality of the algorithms. software components contain desktop technology, engineering, transportation, telecommunications, and bioinformatics, making the e-book particularly precious to practitioners in these parts.

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Additional info for Optimization Techniques for Solving Complex Problems (Wiley Series on Parallel and Distributed Computing)

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S. , Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII , vol. 4725 of SPIE , 2002, pp. 338–345. 14. A. Vieira, B. Ribeiro, and J. C. Neves. A method to improve generalization of neural networks: application to the problem of bankruptcy prediction. In Proceedings of the 7th International Conference on Adaptive and Natural Computing Algorithms 14 15. 16. 17. 18. 19. 20. 21. GENERATING AUTOMATIC PROJECTIONS BY MEANS OF GENETIC PROGRAMMING (ICANNGA’05), Coimbra, Portugal, vol.

8. J. M. Valls, I. M. Galv´an, and P. Isasi. Lazy learning in radial basis neural networks: a way of achieving more accurate models. Neural Processing Letters, 20(2):105–124, 2004. 9. J. M. Valls, I. M. Galv´an, and P. Isasi. LRBNN: a lazy RBNN model. AI Communications, 20(2):71–86, 2007. 10. I. M. Galv´an, P. Isasi, R. Aler, and J. M. Valls. A selective learning method to improve the generalization of multilayer feedforward neural networks. International Journal of Neural Systems, 11:167–157, 2001.

3 EXPERIMENTAL ANALYSIS The lazy strategy described above, with either the Gaussian or the inverse kernel function, has been applied to different RBNN architectures to measure the generalization capability of the networks in terms of the mean absolute error over the test data. We have used three domains to compare the results obtained by both kernel functions when tackling different types of problems. Besides, we have applied both eager RBNN and classical lazy methods to compare our lazy RBNN techniques with the classical techniques.

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