04_Support_Vector_Machines

Ch 4: Support Vector Machines

This chapter shows how to implement various SVM methods with TensorFlow. We first create a linear SVM and also show how it can be used for regression. We then introduce kernels (RBF Gaussian kernel) and show how to use it to split up non-linear data. We finish with a multi-dimensional implementation of non-linear SVMs to work with multiple classes.

1. Introduction

• We introduce the concept of SVMs and how we will go about implementing them in the TensorFlow framework.

2. Working with Linear SVMs

• We create a linear SVM to separate I. setosa based on sepal length and pedal width in the Iris data set.

3. Reduction to Linear Regression

• The heart of SVMs is separating classes with a line. We change tweek the algorithm slightly to perform SVM regression.

4. Working with Kernels in TensorFlow

• In order to extend SVMs into non-linear data, we explain and show how to implement different kernels in TensorFlow.

5. Implementing Non-Linear SVMs

• We use the Gaussian kernel (RBF) to separate non-linear classes.

6. Implementing Multi-class SVMs

• SVMs are inherently binary predictors. We show how to extend them in a one-vs-all strategy in TensorFlow.