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 |
Keywords
- active set
- minimum enclosing ball
- sequential minimal optimization
- Support Vector Machine
ASJC Scopus subject areas
- Computer Networks and Communications
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Lin, J., Song, M., & Furuzuki, T. (2014). An SMO Approach to fast SVM for classification of large scale data. In 2014 International Conference on IT Convergence and Security, ICITCS 2014 [7021735] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITCS.2014.7021735