[Individual Task] SVM Model Implementation

Coding Assignement of GIST Machine Learning course (EC4213). Implementation of Support Vector Machines (SVM) from scratch. The project covers key concepts such as hard margin SVM, soft margin SVM (primal and dual formulations), and the use of kernel tricks to handle non-linear data.

• Course: GIST Machine Learning (EC4213)
• Project Type: SVM Implementation Individual Coding Assignment

An educational project focusing on the implementation of Support Vector Machines (SVM) from scratch. The project covers key concepts such as hard margin SVM, soft margin SVM (primal and dual formulations), and the use of kernel tricks to handle non-linear data.

The detailed code can be found in the link.

Overview

Hard margin SVM

We implemented the process of finding the optimal decision boundary using hinge loss and coordinate gradient descent.

Soft margin SVM

We can find a decision boundary of two classes by solving dual problem. Slack variables allow misclassification.

Kernel Tricks

We implemented various kernel filters to SVM to compare their performance.