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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References
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford Univ. Press, New York (1995)
Duda, O.D., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Sharkey, A.J.C. (ed.): Combining Artificial Neural Nets. Ensemble and Modular Multi-Net Systems. Perspectives in Neural Computing. Springer, London (1999)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (1998)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999)
Kwok, J.T.: Moderating the Output of Support Vector Machine Classifiers. IEEE Trans. on Neural Networks 10(5), 1018–1031 (1999)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Müller, K.-R., et al.: Predicting time series with Support Vector Machines. In: Advances in Kernel Methods–Support Vector Learning, Cambridge, MA (1999)
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© 2005 Springer-Verlag Berlin Heidelberg
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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
Publisher Name: Springer, Berlin, Heidelberg
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