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

共 50 条

[31] Application of Least Squares Support Vector Machine on vehicle recognition [J].

Yang, Kuihe ;

Shan, Ganlin ;

Zhao, Lingling .

ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1, 2006, :217-221

[32] Least Squares Support Vector Machine for Constitutive Modeling of Clay [J].

Zhou, X. ;

Shen, J. .

INTERNATIONAL JOURNAL OF ENGINEERING, 2015, 28 (11) :1571-1578

[33] Fuzzy Least Squares Support Vector Machine with Fuzzy Hyperplane [J].

Chien-Feng Kung ;

Pei-Yi Hao .

Neural Processing Letters, 2023, 55 :7415-7446

[34] Sparse representation for kernel function selection of support vector machine [J].

Liang L. ;

Wu J. ;

Zhong Z. ;

Zhu S. .

International Journal of Autonomous and Adaptive Communications Systems, 2018, 11 (01)

[35] Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach [J].

Zhu, Bangzhu ;

Ye, Shunxin ;

Jiang, Minxing ;

Wang, Ping ;

Wu, Zhanchi ;

Xie, Rui ;

Chevallier, Julien ;

Wei, Yi-Ming .

APPLIED ENERGY, 2019, 233 :196-207

[36] Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction [J].

Samui, Pijush ;

Kurup, Pradeep .

SOFT COMPUTING, 2012, 16 (08) :1347-1351

[37] Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction [J].

Pijush Samui ;

Pradeep Kurup .

Soft Computing, 2012, 16 :1347-1351

[38] Online Parameters Updating Method for Least Squares Support Vector Machine Using Unscented Kalman Filter [J].

Liu, Xiayong ;

Zhou, Shufang ;

Yan, Changguo ;

Luo, Guangyi ;

Zhang, Qiang .

PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, :3323-3328

[39] On system identification based on online least squares support vector machine [J].

Liu, Bin ;

Wang, Zhiping .

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007), 2007,

[40] Vehicle Recognition Based on Least Squares Support Vector Machine Model [J].

Zhao, Lingling ;

Zhu, Yuran ;

Yang, Kuihe .

2007 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-15, 2007, :3123-3126

← 1 2 3 4 5 →