SVMs work by mapping input data into a higher-dimensional space where a linear separator (hyperplane) can be found. This is often achieved using kernel functions such as the polynomial kernel, radial basis function (RBF), and sigmoid kernel. The goal is to choose a hyperplane that maximizes the margin between the closest points of different classes, known as support vectors.
