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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11494669_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)