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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.
  1. 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.
  1. Reduction to Linear Regression
  • The heart of SVMs is separating classes with a line. We change tweek the algorithm slightly to perform SVM regression.
  1. 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.
  1. Implementing Non-Linear SVMs
  • We use the Gaussian kernel (RBF) to separate non-linear classes.
  1. Implementing Multi-class SVMs
  • SVMs are inherently binary predictors. We show how to extend them in a one-vs-all strategy in TensorFlow.