Understanding Support Vector Machines
SVM are known to be difficult to grasp. Many people refer to them as "black box".
This tutorial series is intended to give you all the necessary tools to really understand the math behind SVM.
It starts softly and then get more complicated. But my goal here is to keep everybody on board, especially people who do not have a strong mathematical background.
Read the Support Vector Machine tutorial
If you wish to have an overview of what SVMs are, you can read this article
An overview of Support Vector Machines
SVM R tutorials
R is a good language if you want to experiment with SVM.
So I wrote some introductory tutorials about it.
The article about Support Vector Regression might interest you even if you don't use R.
Text classification tutorials
You can apply SVM to a wide variety of subjects. One of them is text classification. In the following tutorials you will learn how to transform text into data that you can feed to your SVM.
You will then see how to use this data to perform text classification (in R or in C#)
- How to prepare your data for text classification?
- How to classify text in R ?
- How to classify text in C# ?
Another article explains why the linear kernel is often the choice giving the best results in text classification: