Support Vector Machines (SVM), a supervised learning algorithm, are particularly effective in this domain due to their ability to classify data and perform regression tasks with high precision [1].

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 ...

A novel online, i.e. stochastic gradient, learning algorithm in a primal domain is introduced and its performance is compared to the Sequential Minimal Optimization (SMO) based algorithm for training ...

Support Vector Machine (SVM) is often used in regression and classification problems. However, SVM needs to find proper kernel function to solve high-dimensional problems. We propose an improved ...

Abstract The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training ...

The support vector machine algorithm’s objective is to find a hyperplane in an N-dimensional space that distinctly classifies the data points. In SVM, we plot each data point in the dataset in an ...