Support vector machine for the simultaneous approximation of a function and its derivative

被引：4

作者：

Lázaro, M ^{[1
]}

Santamaría, I ^{[1
]}

Perez-Cruz, F ^{[1
]}

Artés-Rodríguez, A ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Dept Teor Senal & Comunicac, Madrid 28911, Spain

来源：

2003 IEEE XIII WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING - NNSP'03 | 2003年

关键词：

D O I：

10.1109/NNSP.2003.1318018

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the problem of simultaneously approximating a function and its derivative is formulated within the Support Vector Machine (SVM) framework. The problem has been solved by using the E-insensitive loss function and introducing new linear constraints in the approximation of the derivative. The resulting quadratic problem can be solved by Quadratic Programming (QP) techniques. Moreover, a computationally efficient Iterative Re-Weighted Least Square (IRWLS) procedure has been derived to solve the problem in large data sets. The performance of the method has been compared with the conventional SVM for regression, providing outstanding results.

引用

页码：189 / 198

页数：10

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