This document discusses support vector machines (SVM), a supervised machine learning algorithm that can be used for classification and regression. It finds the hyperplane that best divides a dataset into classes with the maximum margin or gap between the different classes. Examples are provided to illustrate how SVMs attempt to place a dividing line to maximize the gap between classes of data points. Questions about applying SVMs to non-linearly separable data are answered by explaining the use of kernels to transform the data into a higher dimensional space where a separating hyperplane can be found.

1. Support Vector Machine
(SVM)
N.Nasurudeen Ahamed,
Assistant Professor,
CSE.

2. Support Vector Machine (SVM)
• Definition : It’s basically a Supervised Machine Learning Algorithm, that
can be employed for both classification and regression purposes.
• Applications of SVM :
• Face Detection.
• Text & Hypertext Categorization.
• Classification of Images.

3. Support Vector Machine (SVM)
• SVM : Bases on the idea of finding the Hyper plane that best divides a
dataset into two classes.

4. Support Vector Machine (SVM)

5. Support Vector Machine (SVM)

6. Support Vector Machine (SVM)

7. Support Vector Machine (SVM) - Example

8. Support Vector Machine (SVM) - Example

9. Support Vector Machine (SVM) - Example

10. Support Vector Machine (SVM) – Example 1
• We have 2 colors of balls on the table that we want to separate, we get a
stick and put it on table, pretty well right?

11. Support Vector Machine (SVM) – Example 1
• Some villain comes and places more balls on the table, it kind of works but
one of the balls is on the wrong side and there is probably a better place to
put the stick now.

12. Support Vector Machine (SVM) – Example 1
• SVMs try to put the stick in the best possible place by having as big a gap
on either side of the stick as possible.
• Solution - Next Slide

14. Support Vector Machine (SVM) – Doubt?
• In case my data set is like below :

15. Support Vector Machine (SVM) – Doubt?
• Solution : Transformation, in this method we use Kernel.
• Kernel : A Kernel is a “similarity function” that we provide to a machine
learning algorithm, most commonly, an SVM. It takes two inputs and
outputs how similar they are.