This project presents the very basic theory of linear classifiers, max-margin classifiers and Support Vector Machines and explores the use of Mathematica © to solve the optimization problems that arise.
Following the presentation in [1], this notebook explicitly derives, implements and compares several classifiers, demonstrating them on synthetic 2D-data generated by the user, with visualizations involving direct hyper-parameters manipulations.
The project can be considered a hands-on introduction to the topic.
[1] Nello Cristianini and John Shawe-Taylor. An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge university press, 2000