Support Vector Regression

Support vector machines can be used as a regression method, maintaining all the main features of the algorithm. In the case of regression, a margin of tolerance ε is set in approximation. The goal of SVR is to find a function that has at most ε deviation from the response variable for all the training data, and at the same time is as flat as possible. In other words, we do not care about errors as long as they are less than ε, but will not accept any deviation larger than this.