Abstract
In this work a modification was made on the algorithm of Artificial Neural Networks (NN) Training of the Multilayer Perceptron type (MLP) based on multi-objective optimization (MOBJ), to increase its computational efficiency. Usually, the number of efficient solutions to be generated is a parameter that must be provided by the user. In this work, this number is automatically determined by an algorithm, through the usage of golden section, being generally less when specified, showing a sensible reduction in the processing time and keeping the high generalization capability of the obtained solution from the original method.
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References
Geman, S., Bienenstock, E., Doursat, R.: Neural networks and bias/variance dilemma. Neural Computing 4(1), 1–58 (1992)
Hinton, G.E.: Deterministic boltzmann machine learning performs steepest descent in weight-space. Neural Computation 1, 143–150 (1989)
Girosi, F., Jones, M., Poggio, T.: Regularization theory and neural networks architectures. Neural Computation 7(2), 219–269 (1995)
Teixeira, R.A., Braga, A.P., Saldanha, R.R., Takahashi, R.H.C.: Improving generalization of mlps with multi-objective optimization. Neurocomputing 35(1-4), 189–194 (2000)
Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making: Theory and Methodoly, vol. 8. Elsevier, Horth-Holland (1983)
Shor, N.Z.: Cut-off method with space extension in convex programming problems. Cybernetics 12, 94–96 (1977)
Teixeira, R A.: Treinamento de Redes Neurais Artificiais Atravs de Otimizao Multi-Objetivo: Uma Nova Abordagem para o Equilbrio entre a Polarizao e a Varincia. PhD thesis, Escola de Engenharia da UFMG, Agosto (2001)
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Teixeira, R.A., Braga, A.P., Saldanha, R.R., Takahashi, R.H.C., Medeiros, T.H. (2007). The Usage of Golden Section in Calculating the Efficient Solution in Artificial Neural Networks Training by Multi-objective Optimization. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_30
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DOI: https://doi.org/10.1007/978-3-540-74690-4_30
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