A support vector regression is a concept used in machine learning. It refers to a supervised learning model that analyzes data for classification and regression analysis.
The main difference between a support vector regression and simple regression is that in SVR, the data researcher tries to fit the error within a certain threshold while in simple regression the idea is to minimize the error rate as much as possible.