Learning a function and its derivative forcing the support vector expansion

Marcelino Lázaro, Fernando Pérez-Cruz, Antonio Artés-Rodríguez

Abstract: 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.