Support Vector Machines
Support Vector Machines are machine learning algorithms for binary classification based on recent advances in statistical learning theory. The input is mapped into a high dimensional feature space, in which a linear classifier is constructed that maximizes the margin between the classes and hence generalizes well to unseen data. Learning requires only information about the relative distances of the training instances, so it can be performed for arbitrary distance metrics (called kernels) that may be specific to the application domain. These generalized SVMs are called kernel machines.