Support Vector Machine Implementation

Our Project for the Theoretical Foundations Of Machine Learning course taken during fall 2023 semester.

Implementing the support vector machine algorithm that performs the following tasks:

1. Train and classify for linearly separable case.
2. Train and classify for the non-separable case.
3. Train and classify for non-linearly separable case. [Using Kernels]

In each of the previous three cases:

• Find optimal alphas.
• Calculate W & b .
• Formulate the classifier Function.
• Draw the decision boundary.
• Use an appropriate dataset for each case.

4. Performs analysis based on the VC-dimensional and generalization error.