Support Vector Machines (SVMs) are a versatile and powerful machine learning algorithm that has gained significant popularity for solving classification and regression problems. They have been ...

Support Vector Machines (SVMs) represent a robust and versatile class of machine learning algorithms that have significantly shaped the fields of pattern recognition, classification, and regression.

The goal of a machine learning regression problem is to predict a single numeric value, for example, predicting a person's income based on their age, height, years of education, and so on. There are ...

This article proposes the multiclass proximal support vector machine (MPSVM) classifier, which extends the binary PSVM to the multiclass case. Unlike the one-versus-rest approach that constructs the ...

In this paper, we evaluate the use of an electronic nose (EN) containing 13 conducting polymer gas sensors to discriminate between patterns of volatile organic compounds (VOCs) emitted by plants. The ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...