PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Support vector machine for the simultaneous approximation of a function and its derivative
Marcelino Lazaro, Ignacio Santamaria, Fernando Perez-Cruz and Antonio Artes-Rodriguez
In: IEEE 13th Workshop on Neural Networks for Signal Processing, 2003. NNSP'03, 17-19 Sept. 2003, Toulouse.

Abstract

In this paper, the problem of simultaneously approximating a function and its derivative is formulated within the support vector machine (SVM) framework. The problem has been solved by using the /spl epsiv/-insensitive loss function and introducing new linear constraints in the approximation of the derivative. The resulting quadratic problem can be solved by quadratic programming (QP) techniques. Moreover, a computationally efficient iterative re-weighted least square (IRWLS) procedure has been derived to solve the problem in large data sets. The performance of the method has been compared with the conventional SVM for regression, providing outstanding results.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:526
Deposited By:Fernando Perez-Cruz
Deposited On:24 December 2004