Randomized kernel methods for least-squares support vector machines

被引：1
|
作者：
Andrecut, M.
机构：
[1] Calgary, T3G 5Y8, AB
来源：
INTERNATIONAL JOURNAL OF MODERN PHYSICS C | 2017年 / 28卷 / 02期
关键词：
Kernel methods; multiclass classification; big data sets; EQUATIONS; SPEED;
D O I：
10.1142/S0129183117500152
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The least-squares support vector machine ( LS-SVM) is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the LS-SVM classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods.
引用
收藏
页数：18
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