Mathematical Foundation and Application of Support Vector Machines in Pattern Recognition

Abstract In the realm of pattern recognition, one of the most useful algorithms is Support Vector Machines (SVMs) which is supervised learning model for classification and regression for high dimensional data. This paper explores the mathematical foundation of SVMs, focusing on primal and dual optimization framework, kernel methods, and generalization properties. The paper also presents practical applications of SVMs in pattern recognition, including bioinformatics. We perform the original experiments using synthetic 2D data for hyper plane visualization and gene expression dataset and MNIST dataset to evaluate impact of SVM in accuracy and training time. We demonstrate SVMs’ effectiveness and compare their performance with other classifiers. This work aims to bridge the gap between theoretical rigor and practical utility, offering insights for researchers and practitioners in pattern recognition.