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.
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
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.