Analysis of nonstationary time series using support vector machines

被引：0
|
作者：
Chang, MW [1 ]
Lin, CJ
Weng, RC
机构：
[1] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 106, Taiwan
[2] Natl Chengchi Univ, Dept Stat, Taipei 116, Taiwan
来源：
PATTERN RECOGNITION WITH SUPPORT VECTOR MACHINES, PROCEEDINGS | 2002年 / 2388卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Time series from alternating dynamics have many important applications. In [5], the authors propose an approach to solve the drifting dynamics. Their method directly solves a non-convex optimization problem. In this paper, we propose a strategy which solves a sequence of convex optimization problems by using modified support vector regression. Experimental results showing its practical viability are presented and we also discuss the advantages and disadvantages of the proposed approach.
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页码：160 / 170
页数：11
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