Title :
Learning a function and its derivative forcing the support vector expansion
Author :
Lázaro, Marcelino ; Pérez-Cruz, Fernando ; Artés-Rodríguez, Antonio
Author_Institution :
Dept. de Teoria de la Senal y Comunicaciones, Univ. Carlos III, Madrid, Spain
fDate :
3/1/2005 12:00:00 AM
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.
Keywords :
function approximation; iterative methods; least squares approximations; optimisation; support vector machines; IRWLS; SVM; function approximation; function-derivative learning; iterative reweighted least squares algorithm; kernel-based support vector expansion; optimization problem; support vector machine; Constraint optimization; HEMTs; Helium; Iterative algorithms; Kernel; Least squares approximation; Least squares methods; MESFETs; Predictive models; Support vector machines; Function approximation; IRWLS; SVM; support vectors;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2004.840841
