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Yalsavar, Maryam, Karimaghaee, Paknoosh, Sheikh-Akbari, Akbar, Dehmeshki, Jamshid, Khooban, Mohammad-Hassan and Al-Majeed, Salah 
ORCID: 0000-0002-5932-9658
(2020)
Sliding Mode Control based Support Vector Machine
RBF Kernel Parameter Optimization.
In: IEEE International Conference on Imaging Systems & Techniques, 8-10 December 2019, Abu Dabi, UAE.
ISSN 1558-2809
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Text (conference paper)
7660 Al-Majeed (2019) Sliding Mode Control based Support Vector Machine.pdf - Accepted Version Available under License All Rights Reserved. Download (431kB) | Preview |
Abstract
Support Vector Machine (SVM) is a learning-based algorithm, which is widely used for classification in many applications. Despite its advantages, its application to large scale datasets is limited due to its use of large number of support vectors and dependency of its performance on its kernel parameter. This paper presents a Sliding Mode Control based Support Vector Machine Radial Basis Function’s kernel parameter optimization (SMC-SVM-RBF) method, inspired by sliding mode closed loop control theory, which has demonstrated significantly higher performance to that of the standard closed loop control technique. The proposed method first defines an error equation and a sliding surface and then iteratively updates the RBF’s kernel parameter based on the sliding mode control theory, forcing SVM training error to converge below a predefined threshold value. The closed loop nature of the proposed algorithm increases the robustness of the technique to uncertainty and improves its convergence speed. Experimental results were generated using nine standard benchmark datasets covering wide range of applications. Results show the proposed SMC-SVM-RBF method is significantly faster than those of classical SVM based techniques. Moreover, it generates more accurate results than most of the state of the art SVM based methods.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Support vector machine; Sliding mode control; Radial basis function; Classification; Classification speed |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Marta Kemp |
| Date Deposited: | 28 Nov 2019 10:18 |
| Last Modified: | 13 Mar 2024 13:19 |
| URI: | https://eprints.glos.ac.uk/id/eprint/7660 |
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