SVM

Introduction

Simple Browser Implementation of Support Vector Machine Support Vector Machines (SVM) are a very powerful class of supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. It presents one of the most robust prediction methods, based on the statistical learning framework (VC Theory).

Advantages of using Support Vector Machines

Support Vector Machines provide us with the best decision boundary there is between the two classes. It basically creates the widest street possible in between the classes. As a result, when there is a clear margin of separatio between the classes, SVM works really well.

How does it work

The SVM algorithm is trying to plot a decision boundary separating the Yellow points from the Blue Points. The SVM tries to create the widest possible decision boundary that can be made between the two classes. The SVM implemented here uses the SMO algorithm to find the decision boundary.

How to run?

Follow these steps to run the example on your local device:

• Clone this repository onto your machine using git clone https://github.com/Garg-Doppler/SVM.git
• cd into the local repository.
• Run yarn install in the terminal to install dependencies.
• Run yarn watch to run the example.

Check out the website here