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 ...

This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction ...

Five groups of different feature inputs are constructed based on the cumulative feature importance, and the original support vector machine regression (SVR) algorithm is applied to perform SOH ...

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 ...