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Fast Classification with Neural Networks via Confidence Rating

  • J. Arenas-García
  • V. Gómez-Verdejo
  • S. Muñoz-Romero
  • M. Ortega-Moral
  • A. R. Figueiras-Vidal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • J. Arenas-García
    • 1
  • V. Gómez-Verdejo
    • 1
  • S. Muñoz-Romero
    • 1
  • M. Ortega-Moral
    • 1
  • A. R. Figueiras-Vidal
    • 1
  1. 1.Department of Signal Theory and CommunicationsUniversidad Carlos III de MadridLeganés-MadridSpain

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