An explicit algorithm for training support vector machines

被引：14

作者：

Mattera, D ^{[1
]}

Palmieri, F

Haykin, S

机构：

[1] Univ Naples Federico II, Dipartimento Ingn Elettron & Telecomun, I-80125 Naples, Italy

[2] McMaster Univ, Commun Res Lab, Hamilton, ON L8S 4K1, Canada

来源：

IEEE SIGNAL PROCESSING LETTERS | 1999年 / 6卷 / 09期

关键词：

neural networks; nonlinear estimation; nonlinear systems;

D O I：

10.1109/97.782071

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：

The support vector machine (SVM) constitutes one of the most powerful methods for constructing a mathematical model on the basis of a given number of training examples. SVM training requires that we solve a quadratic optimization problem; this step is usually performed by means of existing software packages, Such a black-box approach may be undesirable. In this paper we introduce a simple iterative algorithm for SVM training which compares well with some typical software packages, can be simply implemented, and has minimal memory requirements, It addresses the problem of regression estimation and utilizes ideas similar to those proposed in [1] for trraining binary SVM.

引用

页码：243 / 245

页数：3

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