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Conference papers

Parameterized normalization : application to wavelet networks

Richard Baron 1 Bernard Girau 2
2 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Normalization is studied in the case of wavelet networks, and we derive a dynamic interpretation of its influence, which can be extended to several neural models. We show that data normalization may be simulated and parameterized to avoid data preprocessing, so that the normalization process becomes either tunable or dynamically adaptable. The main benefit of the proposed methods is a big reduction of the time of convergence on a satisfactory classification rate.
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Submitted on : Thursday, October 19, 2006 - 3:40:17 PM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM


  • HAL Id : inria-00108033, version 1


Richard Baron, Bernard Girau. Parameterized normalization : application to wavelet networks. IJCNN, 1998, none, 5 p. ⟨inria-00108033⟩



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