Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are ...

Abstract: Support vector machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem. The inverse problem of SVM is how to split a given dataset ...

Abstract: This paper relies on the Mean Decision Rule (MDR) method for solving large-scale binary SVM problems. It consists in taking small random samples of the full dataset and separate training for ...

In this paper, a novel formulation, smooth entropy support vector regression (SESVR), is proposed, which is a smooth unconstrained optimization reformulation of the traditional linear programming ...

This project demonstrates an optimization workflow involving multiple linear regression, polynomial regression using Support Vector Machines (SVM), and optimization techniques including Simplex and ...