Machine Learning Algorithms - Second Edition

Support Vector Regression

SVMs can also be efficiently employed for regression tasks. However, it's necessary to consider a slightly different loss function that can take into account the maximum discrepancy between prediction and the target value. The most common choice is the ε-insensitive loss (which we've already seen in passive-aggressive regression):

In this case, we consider the problem as one of a standard SVM where the separating hyperplane and the (soft) margins are built sequentially to minimize the prediction error. In the following diagram, there's a schema representing this process:

Example of Support Vector Regression; the ...