Consistency of Derivative Based Functional Classifiers on Sampled Data

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.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Fabrice Rossi, Nathalie Villa
• Keywords: ESANN 2008, Neural Networks, Spectrometric Ones, Spline Based Approach, Strong Theoretical Background