Support Vector Machine

Support vector machines model the relationship between an input variable \({\bf x}\) and a continous output variable \(y\) by finding a hyperplane separating examples belonging to different classes with maximal margin.

SVM(X::Matrix, y::Vector; kernel::Symbol=:linear)

Initialize an SVM object with a data matrix \({\bf X} \in \mathbb{R}^{n\times m}\), a label binary label vector \({\bf y} \in \mathbb{R}^{n}\) of \(n\) \(m\)-dimensional examples, and a kernel function.

Implements: optimize