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Abstract: A novel hybrid evolutionary approach is presented in this paper for improving the performance of neural network classifiers in slowly varying environments. For this purpose, we investigate a coupling of Differential Evolution Strategy and Stochastic Gradient Descent, using both the global search capabilities of Evolutionary Strategies and the effectiveness of on-line gradient descent. The use of Differential Evolution Strategy is related to the concept of evolution of a number of individuals... (Update)
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.... Better generalization results for this di#cult problem can be achieved using specially designed training algorithms (see for example [7]) Algorithm Generalization BP 78.09 BPM 78.09 ABP 79.10 NMBBP 83.91 NMBPVS 85.12 Table 2: Generalization Results. Finally, we...
.... the following relation is used: logln So I (n = to enat = lim , n nat where n is the divergence of the trajectories over the time [7]. In Figure 1, the seismic events (with magnitude greater than 2 degrees of Richter scale) that occurred in Greece the last twenty years are...
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BibTeX entry: (Update)
G.D. Magoulas, V.P. Plagianakos, and M.N. Vrahatis, Hybrid Methods Using Evolutionary Algorithms for On--line Training, In Proc. of the IEEE International Joint Conference on Neural Networks (IJCNN'2001), Washington D.C., (2001). http://citeseer.ist.psu.edu/magoulas01hybrid.html More
@inproceedings{ magoulas01hybrid,
author = "G. Magoulas and V. Plagianakos and M. Vrahatis",
title = "Hybrid Methods Using Evolutionary Algorithms for On--line Training",
booktitle = "Proc. of the IEEE International
Joint Conference on Neural Networks (IJCNN'2001), Washington D.C.",
year = "2001",
url = "citeseer.ist.psu.edu/magoulas01hybrid.html" }
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