Abstract
We propose a new cost function for neural network classification: the error density at the origin. This method provides a simple objective function that can be easily plugged in the usual backpropagation algorithm, giving a simple and efficient learning scheme. Experimental work shows the effectiveness and superiority of the proposed method when compared to the usual mean square error criteria in four well known datasets.
This work was supported by the Portuguese FCT-Fundação para a Ciência e a Tecnologia (project POSI/EIA/56918/2004). First author is also supported by FCT’s grant SFRH/BD/16916/2004.
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© 2005 Springer-Verlag Berlin Heidelberg
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Silva, L.M., Alexandre, L.A., de Sá, J.M. (2005). Neural Network Classification: Maximizing Zero-Error Density. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_14
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DOI: https://doi.org/10.1007/11551188_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
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