Implementing SVM From Scratch

The support vector machine (SVM), developed by the computer science community in the 1990s, is a supervised learning algorithm commonly used and originally intended for a binary classification setting. It is often considered one of the best "out of the box" classifiers. The SVM is a generalization of the simple yet elegant algorithm called the maximal margin classifier. This classifier, however, cannot be applied in every situation since it relies heavily on the assumption that the dataset is linearly separable -- thus, several extensions exist. Note: In the following, we will only cover the maximal margin classifier, purposely avoiding the different extensions.