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I'm looking for good resources regarding Support Vector Machines, or suggestions where to start learning SVM.

Already used references:

  • Stanford ML course by Andrew Ng is great place to star

  • A Tutorial on Support Vector Machines for Pattern Recognition, Burges, 1998
    SVM tutorials

  • Neural Networks and Learning Machines, Third Edition Learning with Kernels - SVM, A. Smola

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    $\begingroup$ That question is awfully general. $\endgroup$ – Raphael♦ Sep 26 '12 at 20:45
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    $\begingroup$ It is general question. I can't ask for some help from people with experience ? $\endgroup$ – dzeno Sep 26 '12 at 20:50
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    $\begingroup$ You certainly can, but without giving any information of your background and specific interests, it's just not a good question (for Stack Exchange). $\endgroup$ – Raphael♦ Sep 28 '12 at 11:21
  • $\begingroup$ I will keep that in mind next time. $\endgroup$ – dzeno Sep 28 '12 at 15:18
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    $\begingroup$ Note that you can still improve this question by editing it. $\endgroup$ – Raphael♦ Sep 28 '12 at 16:46
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There are tons of tutorials for various backgrounds. What is your background? Here is one list of tutorials: http://www.kernel-machines.org/tutorials

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  • $\begingroup$ Thank you! I'm CS student. I'm new to Machine Learning, and I'm interested particularly in SVM. I tried with 'Learning with Kernels' by B.Schölkopf and A.Smola but I need good resources to get right background. Thanks. $\endgroup$ – dzeno Sep 26 '12 at 20:39
  • $\begingroup$ Understanding the intuition is rather easy, but you will need some linear algebra, probability and optimization background to understand how it works. $\endgroup$ – Bitwise Sep 26 '12 at 20:45
  • $\begingroup$ Yes, of course. I'm currently watching ML course by Andrew Ng: academicearth.org/courses/machine-learning and I'm looking for companion book. $\endgroup$ – dzeno Sep 26 '12 at 21:18
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Check out Metacademy. It is a wonderful learning guide for many topics in machine learning. Specifically, this page lists various resources for learning about support vector machines.

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Some background info: You may want to start by studying perceptrons and the learning algorithm (a building block for SVMs). It may also be useful to read up a bit on kernel methods (dealing with high dimensional data) in machine learning and Lagrange multipliers and how to apply them in different optimization tasks.

For understanding the theory: There is a really good tutorial on SVMs which goes through (at least briefly) a lot of the background material and gives a good intro to SVMs.

"Pattern Recognition" by S. Theodoridis and K. Koutroumbas is a really good reference for SVMs and all of the theory behind a lot of pattern recognition related topics.

In practice: Have a look at LibSVM and start playing around with some datasets from UC Irvine's machine learning data repository. Play around with different kernel functions and see how the classification accuracy changes under various conditions for different datasets.

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The following book is a very good and complete source on Machine Learning:

Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists.

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  • $\begingroup$ How can it be complete if it only covers neural networks? It seems to cover SVM, though. $\endgroup$ – Raphael♦ Sep 29 '12 at 12:05
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    $\begingroup$ @Raphael: It includes all areas one needs to know about machine learning at OP's level. I recommend you to read the book before commenting. $\endgroup$ – Gigili Sep 29 '12 at 12:33
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In addition to listed references, I would recommend the following:

  • A. Nefedov. Support Vector Machines: A Simple Tutorial, 2016
  • N. Cristianini, J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2003
  • B. Schölkopf, C.Burges, A. Smola. Advances in Kernel Methods: Support Vector Learning, MIT Press, 1999
  • V. Vapnik. Statistical Learning Theory, John Wiley & Sons, 1998
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