Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters

被引：0
|
作者：
Chaoyu Yang
Jie Yang
Jun Ma
机构：
[1] Anhui University of Science and Technology,School of Economics and Management
[2] University of Wollongong,School of Computing and Information Technology, Faculty of Engineering and Information Sciences
[3] Sydney Trains,Operations Delivery Division
来源：
International Journal of Computational Intelligence Systems | 2020年 / 13卷
关键词：
Least squares support vector machine; Sparse representation; Dictionary learning; Kernel parameter optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we propose an efficient Least Squares Support Vector Machine (LS-SVM) training algorithm, which incorporates sparse representation and dictionary learning. First, we formalize the LS-SVM training as a sparse representation process. Second, kernel parameters are adjusted by optimizing their average coherence. As such, the proposed algorithm addresses the training problem via generating the sparse solution and optimizing kernel parameters simultaneously. Experimental results demonstrate that the proposed algorithm is capable of achieving competitive performance compared to state-of-the-art approaches.
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收藏
页码：212 / 222
页数：10
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