In this letter, we consider the binary classification problem via distributed Support Vector Machines (SVMs), where the idea is to train a network of agents, with limited share of data, to ...

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].

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 ...

The Support Vector methods was proposed by V.Vapnik in 1965, when he was trying to solve problems in pattern recognition. In 1971, Kimeldorf proposed a method of constructing kernel space based on ...

The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled ...

Abstract Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets.