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Consistency of Derivative Based Functional Classifiers on Sampled Data

Abstract : In some applications, especially spectrometric ones, curve classifiers achieve better performances if they work on the $m$-order derivatives of their inputs. This paper proposes a smoothing spline based approach that give a strong theoretical background to this common practice.
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https://hal.archives-ouvertes.fr/hal-00666609
Contributor : Nathalie Vialaneix <>
Submitted on : Sunday, February 5, 2012 - 7:35:20 PM
Last modification on : Thursday, March 5, 2020 - 5:57:24 PM
Long-term archiving on: : Sunday, May 6, 2012 - 2:22:04 AM

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  • HAL Id : hal-00666609, version 1

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Fabrice Rossi, Nathalie Villa-Vialaneix. Consistency of Derivative Based Functional Classifiers on Sampled Data. ESANN 2008, 16th European Symposium on Artificial Neural Networks, Apr 2008, Bruges, Belgium. pp.445-450. ⟨hal-00666609⟩

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