The support vector machine (SVM) algorithm is a supervised learning technique. To understand this algorithm, take a look at the following diagram for the optimal hyperplane and maximum margin:
In this classification problem, we only have two classes that exist for many possible solutions to a problem. As shown in the preceding diagram, the SVM classifies these objects by calculating an optimal hyperplane and maximizing the margins between the classes. Both of these things will differentiate the classes to the maximum extent. Samples that are placed closest to the margin are known as support vectors. The problem is then treated as an optimization problem and can be solved by optimization techniques, the most common one being the use of Lagrange multipliers.
Even in a separable linear problem, as shown in the preceding diagram, sometimes, it is not always...
