Learning a function and its derivative forcing the support vector expansion

被引：3

作者：

Lázaro, M

Pérez-Cruz, F

Artés-Rodríguez, A

机构：

[1] Univ Carlos III Madrid, Dept Teoria Senal & Comunicac, Madrid, Spain

[2] UCL, Gatsby Computat Neurosci Unit, London WC1N 3AR, England

来源：

IEEE SIGNAL PROCESSING LETTERS | 2005年 / 12卷 / 03期

关键词：

function approximation; IRWLS; support vectors; SVM;

D O I：

10.1109/LSP.2004.840841

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：194 / 197

页数：4

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