A semi-supervised online sequential extreme learning machine method

doi:10.1016/j.neucom.2015.04.102

	A semi-supervised online sequential extreme learning machine method
	Jia, Xibin 1; Wang, Runyuan 1; Liu, Junfa 2; Powers, David M. W.1,3
	2016-01-22
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	174 页码:168-178
摘要	This paper proposes a learning algorithm called Semi-supervised Online Sequential ELM, denoted as SOS-ELM. It aims to provide a solution for streaming data applications by learning from just the newly arrived observations, called a chunk. In addition, SOS-ELM can utilize both labeled and unlabeled training data by combining the advantages of two existing algorithms: Online Sequential ELM (OS-ELM) and Semi-Supervised ELM (SS-ELM). The rationale behind our algorithm exploits an optimal condition to alleviate empirical risk and structure risk used by SS-ELM, in combination with block calculation of matrices similar to OS-ELM. Efficient implementation of the SOS-ELM algorithm is made viable by an additional assumption that there is negligible structural relationship between chunks from different times. Experiments have been performed on standard benchmark problems for regression, balanced binary classification, unbalanced binary classification and multi-class classification by comparing the performance of the proposed SOS-ELM with OS-ELM and SS-ELM. The experimental results show that the SOS-ELM outperforms OS-ELM in generalization performance with similar training speed, and in addition outperforms SS-ELM with much lower supervision overheads. (C) 2015 Elsevier B.V. All rights reserved.
关键词	Online Sequential ELM (OS-ELM) Semi-supervised ELM (SS-ELM) Semi-supervised online sequential ELM (SOS-ELM)
DOI	10.1016/j.neucom.2015.04.102
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[61375059] ; Natural Science Foundation of China[61175115] ; Beijing Natural Science Foundation[4122004] ; Beijing Natural Science Foundation[4152005] ; Specialized Research Fund for the Doctoral Program of Higher Education[20121103110031] ; Importation and the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions[CITTCD201304035] ; Special training program for construction of teachers of Beijing High education - abroad training program for Senior visiting scholars by Beijing high education teacher training center[067145301400] ; Jing-Hua Talents Project of Beijing University of Technology[2014-JH-L06] ; International Communication Ability Development Plan for Young Teachers of Beijing University of Technology[2014-16]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000367276700016
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：40[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8992
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jia, Xibin
作者单位	1.Beijing Univ Technol, Beijing Municipal Key Lab Multimedia & Intelligen, Beijing 100124, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Flinders Univ S Australia, Ctr Knowledge & Interact Technol, Bedford Pk, SA 5042, Australia
推荐引用方式 GB/T 7714	Jia, Xibin,Wang, Runyuan,Liu, Junfa,et al. A semi-supervised online sequential extreme learning machine method[J]. NEUROCOMPUTING,2016,174:168-178.
APA	Jia, Xibin,Wang, Runyuan,Liu, Junfa,&Powers, David M. W..(2016).A semi-supervised online sequential extreme learning machine method.NEUROCOMPUTING,174,168-178.
MLA	Jia, Xibin,et al."A semi-supervised online sequential extreme learning machine method".NEUROCOMPUTING 174(2016):168-178.