Machine Learning Followup

It is my great pleasure to give an introduction to the machine learning algorithms. I went through the blog posts and it seems that some of you are confused about support vector machines (SVM), which is difficult to understand without going into math derivations. A very good tutorial slides on SVM can be find at

http://www.autonlab.org/tutorials/svm15.pdf

and there are a lot of other machine learning tutorials at

http://www.autonlab.org/tutorials/index.html

The most widely used SVM software is LIBSVM:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

and there is a Java demo on the web site so that you can play with. Moreover, there is a short introduction to SVM at:

http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

For a complete understanding of SVM, you can refer to this long tutorial paper, which is one of the classic paper in machine learning:

http://research.microsoft.com/en-us/um/people/cburges/papers/SVMTutorial.pdf

It turns out that the SVM decision boundary only depends on the data points close to the boundary. So it seems that the boundary is supported by only these vectors around the boundary, and thus it is called SVM. This is clear if you go through the slides mentioned above (http://www.autonlab.org/tutorials/svm15.pdf). I talked to one of the inventors of SVM, Bernhard Schölkopf, and he said that initially, they plan to call it support vector networks. Since "network" reminds many people about neural networks, and thus it is instead called "machines".

Posted by

Shuiwang Ji