Abstract
We present a novel technique to reduce the computational burden associated to the operational phase of neural networks. To get this, we develop a very simple procedure for fast classification that can be applied to any network whose output is calculated as a weighted sum of terms, which comprises a wide variety of neural schemes, such as multi-net networks and Radial Basis Function (RBF) networks, among many others. Basically, the idea consists on sequentially evaluating the sum terms, using a series of thresholds which are associated to the confidence that a partial output will coincide with the overall network classification criterion. The possibilities of this strategy are well-illustrated by some experiments on a benchmark of binary classification problems, using RealAdaboost and RBF networks as the underlying technologies.
This work has been partly supported by CICYT grant TIC2002-03713.
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Arenas-García, J., Gómez-Verdejo, V., Muñoz-Romero, S., Ortega-Moral, M., Figueiras-Vidal, A.R. (2005). Fast Classification with Neural Networks via Confidence Rating. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_76
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DOI: https://doi.org/10.1007/11494669_76
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