A semi-supervised online sequential extreme learning machine method | |
Jia, Xibin2; Wang, Runyuan2; Liu, Junfa3; Powers, David M. W.1,2 | |
刊名 | NEUROCOMPUTING |
2016-01-22 | |
卷号 | 174页码:168-178 |
关键词 | Online Sequential ELM (OS-ELM) Semi-supervised ELM (SS-ELM) Semi-supervised online sequential ELM (SOS-ELM) |
ISSN号 | 0925-2312 |
DOI | 10.1016/j.neucom.2015.04.102 |
英文摘要 | 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. |
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 |
语种 | 英语 |
出版者 | ELSEVIER SCIENCE BV |
WOS记录号 | WOS:000367276700016 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/8991] |
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.Flinders Univ S Australia, Ctr Knowledge & Interact Technol, Bedford Pk, SA 5042, Australia 2.Beijing Univ Technol, Beijing Municipal Key Lab Multimedia & Intelligen, Beijing 100124, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China |
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. |
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