Support Vector Machine and Its Difficulties From Control Field of View

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 stateof-the-art SVM-based techniques while using a significantly smaller number of support vectors

Sliding Mode Control based Support Vector Machine RBF Kernel Parameter Optimization

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

Kernel Parameter Optimization for Support Vector Machine Based on Sliding Mode Control

Support Vector Machine (SVM) is a supervised machine learning algorithm, which is used for robust and accurate classification. Despite its advantages, its classification speed deteriorates due to its large number of support vectors when dealing with large scale problems and dependency of its performance on its kernel parameter. This paper presents a kernel parameter optimization algorithm for Support Vector Machine (SVM) based on Sliding Mode Control algorithm in a closed-loop manner. The proposed method defines an error equation and a sliding surface, iteratively updates the Radial Basis Function (RBF) kernel parameter or the 2-degree polynomial kernel parameters, forcing SVM training error to converge below a threshold value. Due to the closed-loop nature of the proposed algorithm, key features such as robustness to uncertainty and fast convergence can be obtained. To assess the performance of the proposed technique, ten standard benchmark databases covering a range of applications were used. The proposed method and the state-of-the-art techniques were then used to classify the data. Experimental results show the proposed method is significantly faster and more accurate than the anchor SVM technique and some of the most recent methods. These achievements are due to the closed-loop nature of the proposed algorithm, which significantly has reduced the data dependency of the proposed method