Derivation, Optimization, and Comparative Analysis of Support Vector Machines Application to Multi-Class Image Data

Support Vector Machines (SVM) have emerged as a powerful and versatile machine learning technique for solving classification and regression problems. This paper presents a thorough review of SVM, encompassing its motivation, derivation of the optimization problem, the utilization of kernels for data transformation, and a comprehensive analysis of solution methods. The review is supported by experiments conducted on a data set derived from the Traffic Sign data set. The motivation for SVM lies in its ability to address complex classification tasks by transforming the data into a higher-dimensional feature space. This is particularly beneficial for data sets derived from multiple sources. The findings presented in this paper contribute to a better understanding of SVM's capabilities.