# Lesson 10: Support Vector Machines

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### Introduction

 Key Learning Goals for this Lesson: Understand how the maximal margin classifier works for datasets in which two classes are separable by a linear boundary. Understand the support vector classifier, which extends the maximal margin classifier to work with overlapping classes. Understand support vector machines, which extend support vector classifiers to accomodate non-linear class boundaries. Textbook reading: Chapter 9: Support Vector Machines (exclude Section 9.5).

### Overview of the Algorithm

Support vector machines are a class of statistical models first developed in the mid-1960s by Vladimir Vapnik. In later years, the model has evolved considerably into one of the most flexible and effective machine learning tools available. It is a supervised learning algorithm which can be used to solve both classification and regression problem, even though the current focus is on classification only.

To put it in a nutshell, this algorithm looks for a linearly separable hyperplane, or a decision boundary separating members of one class from the other. If such a hyperplane exists, the work is done! If such a hyperplane does not exist, SVM uses a nonlinear mapping to transform the training data into a higher dimension. Then it searches for the linear optimal separating hyperplane. With an appropriate nonlinear mapping to a sufficiently high dimension, data from two classes can always be separated by a hyperplane. The SVM algorithm finds this hyperplane using support vectors and margins. As a training algorithm, SVM may not be very fast compared to some other classification methods, but owing to its ability to model complex nonlinear boundaries, SVM has high accuracy. SVM is comparatively less prone to overfitting. SVM has successfully been applied to handwritten digit recognition, text classification, speaker identification etc.

After completing the reading for this lesson, please finish the Quiz and R Lab on ANGEL (check the course schedule for due dates).