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C
Fontes, T., Silva, L. M., Silva, M. P., Barros, N., & Carvalho, A. C. (2014). Can artificial neural networks be used to predict the origin of ozone episodes?. Science of the Total Environment, 488-489(1), 197 - 207.
D
Silva, L. M., Marques de Sá, J., & Alexandre, L. A. (2008). Data classification with multilayer perceptrons using a generalized error function. Neural NetworksNeural Netw., 21(9), 1302 - 1310.
E
Silva, L. M., Felgueiras, C. S., Alexandre, L. A., & Marques de Sá, J. (2006). Error entropy in classification problems: A univariate data analysis. Neural ComputationNeural Comp., 18(9), 2036 - 2061.
I
Silva, L. M., Embrechts, M., Santos, J. M., & De Sá, J. M. (2008). The influence of the risk functional in data classification with MLPs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci. (Vol. 5163 LNCS, pp. 185 - 194).
M
Silva, L. M., Marques de Sá, J., & Alexandre, L. A. (2010). The MEE principle in data classification: A perceptron-based analysis. Neural ComputationNeural Comp., 22(10), 2698 - 2728.
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Silva, L. M., Alexandre, L. A., & De Sá, J. M. (2005). Neural network classification: Maximizing zero-error density. In S. S., M., S., C., A., & P., P. (Eds.), Lecture Notes in Computer ScienceLect. Notes Comput. Sci. (Vol. 3686, pp. 127 - 135).
Silva, L. M., Marques de Sá, J., & Alexandre, L. A. (2006). New developments of the Z-EDM algorithm. In Proceedings - ISDA 2006: Sixth International Conference on Intelligent Systems Design and ApplicationsProc. ISDA Sixth Int. Conf. Intelligent Syst. Design Applic. (Vol. 1, pp. 1067 - 1072).