Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters

被引：3

作者：

Yang, Chaoyu ^{[1
]}

Yang, Jie ^{[2
]}

Ma, Jun ^{[3
]}

机构：

[1] Anhui Univ Sci & Technol, Sch Econ & Management, Huainan 232001, Peoples R China

[2] Univ Wollongong, Fac Engn & Informat Sci, Sch Comp & Informat Technol, Wollongong, NSW 2522, Australia

[3] Sydney Trains, Operat Delivery Div, Alexandria, NSW 2015, Australia

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2020年 / 13卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Least squares support vector machine; Sparse representation; Dictionary learning; Kernel parameter optimization; OPTIMIZED PROJECTIONS; DICTIONARY;

D O I：

10.2991/ijcis.d.200205.001

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, 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. (C) 2020 The Authors. Published by Atlantis Press SARL.

引用

页码：212 / 222

页数：11

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