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12

ESANN
2008

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Consistency of Derivative Based Functional Classifiers on Sampled Data

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Consistency of Derivative Based Functional Classifiers on Sampled Data

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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.

Fabrice Rossi, Nathalie Villa

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ESANN 2008 | Neural Networks | Spectrometric Ones | Spline Based Approach | Strong Theoretical Background |

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	ESANN
Authors	Fabrice Rossi, Nathalie Villa

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