PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning a Function and its Derivatives forcing the Support Vector Expansion
Marcelino Lazaro, Fernando Perez-Cruz and Antonio Artes
IEEE Signal Processing Letters Volume 12, Number 3, pp. 194-197, 2005.


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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1176
Deposited By:Fernando Perez-Cruz
Deposited On:19 November 2005