Support Vector Machines Explained

Support vector machines (SVMs) are a popular linear classifier, the current version of which was developed by Vladimir Vapnik and Corinna Cortes. SVMs are supervised learning models, meaning sample data must be labeled, that can be applied to almost any type of data.

They are especially effective at classification, numeral prediction, and pattern recognition tasks. SVMs find a line (or hyperplane in…