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
收藏
页码：194 / 197
页数：4
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