Most of the applications using support vector machines are related to classification. However, the same technique can be applied to regression problems. Luckily, as with classification, LIBSVM supports two formulations for support vector regression:
∈-VR (sometimes called C-SVR)
υ-SVR
For the sake of consistency with the two previous cases, the following test uses the ∈ (or C) formulation of the support vector regression.
The SVR introduces the concept of error insensitive zone and insensitive error, ε. The insensitive zone defines a range of values around the predictive values, y(x). The penalization component C does not affect the data point {xi,yi} that belongs to the insensitive zone [8:14].
The following diagram illustrates the concept of an error insensitive zone using a single variable feature x and an output y. In the case of a single variable feature, the error insensitive zone is a band of width 2ε (ε is known as the insensitive error). The insensitive...