Learning a function and its derivative forcing the support vector expansion

doi:10.1109/LSP.2004.840841

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Learning a function and its derivative forcing the support vector expansion

2005-03-01

PRE

Marcelino Lázaro

Fernando Pérez‐Cruz

Antonio Artés-Rodrı́guez

DOI：10.1109/LSP.2004.840841

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Abstract

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In this paper, a new method for the simultaneous learning of a function and its derivative is presented. The method, setting out the problem inside of the Support Vector Machine (SVM) framework, relies on the kernel-based Support Vector expansion. The resultant optimization problem is solved by a computationally efficient Iterative Re-Weighted Least Squares (IRWLS) algorithm.

Keywords:

function approximation

IRWLS

support vectors

SVM

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