SVM – Understanding the math – Part 1 by Alexandre Kowalczy. (Part 2)
The first two tutorials of a series on Support Vector Machines (SVM) and their use in data analysis.
If you shudder when you read:
The objective of a support vector machine is to find the optimal separating hyperplane which maximizes the margin of the training data.
you won’t after reading these tutorials. Well written and illustrated.
If you think about it, math symbolism is like programming. It is a very precise language written with a great deal of economy. Which makes it hard to understand for the uninitiated. The underlying ideas, however, can be extracted and explained. That is what you find here.
Want to improve your understanding of what appears on the drop down menu as SVM? This is a great place to start!
PS: A third tutorial is due out soon