Abstract
In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.
| Original language | English |
|---|---|
| Title of host publication | 2014 International Conference on IT Convergence and Security, ICITCS 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781479965410 |
| DOIs | |
| Publication status | Published - 2014 Jan 23 |
| Event | 4th 2014 International Conference on IT Convergence and Security, ICITCS 2014 - Beijing, China Duration: 2014 Oct 28 → 2014 Oct 30 |
Other
| Other | 4th 2014 International Conference on IT Convergence and Security, ICITCS 2014 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 14/10/28 → 14/10/30 |
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Keywords
- active set
- minimum enclosing ball
- sequential minimal optimization
- Support Vector Machine
ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
An SMO Approach to fast SVM for classification of large scale data. / Lin, Juanxi; Song, Mengnan; Furuzuki, Takayuki.
2014 International Conference on IT Convergence and Security, ICITCS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 7021735.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An SMO Approach to fast SVM for classification of large scale data
AU - Lin, Juanxi
AU - Song, Mengnan
AU - Furuzuki, Takayuki
PY - 2014/1/23
Y1 - 2014/1/23
N2 - In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.
AB - In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.
KW - active set
KW - minimum enclosing ball
KW - sequential minimal optimization
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=84988259230&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988259230&partnerID=8YFLogxK
U2 - 10.1109/ICITCS.2014.7021735
DO - 10.1109/ICITCS.2014.7021735
M3 - Conference contribution
AN - SCOPUS:84988259230
BT - 2014 International Conference on IT Convergence and Security, ICITCS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -