Support vector machines, presented for the problem of identifying two groups of points on the plane

Abstract

SVM (Support Vector Machine) is a concept in statistics and computer science for a set of supervised learning methods related to each other for classification and regression analysis. SVM is a binary classification algorithm, Support vector machine (SVM) to build a hyperplane to classify the data set into two separate classes. A hyperplane is a function similar to the line equation, y = ax + b. In fact, if we need to classify a dataset with only two features, the hyperplane is now a straight line. In terms of ideas, SVM uses tricks to map the original dataset to more dimensional spaces. Once mapped to a multidimensional space, SVM will review and select the most suitable superlattice to classify that data set.