Support vector machine and its difficulties from control field of view

Yalsavar, Maryam, Karimaghaei, Paknoosh, Sheikh-Akbari, Akbar, Shukla, Pancham and Setoodeh, Peyman (2021) Support vector machine and its difficulties from control field of view. Transactions of the Institute of Measurement and Control, 43 (9). pp. 1833-1842. ISSN 0142-3312

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Abstract / Description

The application of the Support Vector Machine (SVM) classification algorithm to large-scale datasets is limited due to its use of a large number of support vectors and dependency of its performance on its kernel parameter. In this paper, SVM is redefined as a control system and Iterative Learning Control (ILC) method is used to optimize SVM’s kernel parameter. The ILC technique first defines an error equation and then iteratively updates the kernel function and its regularization parameter using the training error and the previous state of the system. The closed-loop structure 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 a wide range of applications. Experimental results show that the proposed method generates superior or very competitive results in term of accuracy than those of classical and state-of-the-art SVM-based techniques while using a significantly smaller number of support vectors.

Item Type: Article
Uncontrolled Keywords: support vector machine (SVM); iterative learning control; closed loop structure; classification speed
Subjects: 600 Technology > 620 Engineering & allied operations
Department: School of Computing and Digital Media
Depositing User: Panchamkumar Shukla
Date Deposited: 17 Nov 2020 17:19
Last Modified: 28 May 2021 09:06


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