Support Vector Machines
When dealing with data that is linearly separable, the goal of the Support Vector Machine (SVM) learning algorithm is to find the boundary between classes so that there are fewer misclassification errors. However, the problem is that there could be several decision boundaries (B1, B2), as you can see in the following figure:
Figure 8.1: Multiple decision boundary
As a result, the question arises as to which of the boundaries is better, and how to define better. The solution is to use a margin as the optimization objective. A margin can be described as the distance between the boundary and two points (from different classes) lying closest to the boundary. Figure 8.2 gives a nice visual definition of the margin.
The objective of the SVM algorithm is to maximize the margin. You will go over the intuition behind maximizing the margin in the next section. For now, you need to understand that the objective of an SVM linear classifier is to increase the width...
