Mathematical Foundation and Application of Support Vector Machines in Pattern Recognition

Author(s)	Dr. Dr. Sunita S. Patil
Country	India
Abstract	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.
Keywords	Keywords: Support Vector Machines, Pattern Recognition, Machine Learning, Kernel Methods, Optimization
Field	Mathematics
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-08-13
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.53442
Short DOI	https://doi.org/g9w5bv

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research