Multi-dimensional Function Approximation and Regression Estimation

Authors:
Fernando Pérez-Cruz;Gustavo Camps-Valls;Emilio Soria-Olivas;Juan José Pérez-Ruixo;Aníbal R. Figueiras-Vidal;Antonio Artés-Rodríguez
Affiliations:
-;-;-;-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 1
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Abstract

In this communication, we generalize the Support Vector Machines (SVM) for regression estimation and function approximation to multi-dimensional problems. We propose a multi-dimensional Support Vector Regressor (MSVR) that uses a cost function with a hyperspherical insensitive zone, capable of obtaining better predictions than using an SVM independently for each dimension. The resolution of the MSVR is achieved by an iterative procedure over the Karush-Kuhn-Tucker conditions. The proposed algorithm is illustrated by computers experiments.