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I have been trying to grasp the basics of Support Vector Machines, and downloaded and read many online articles. But still am not able to grasp it.
I would like to know, if there are some
or something, that you can think of, and that will enable me to learn SVM Basics easily.
PS: I somehow managed to learn PCA (Principal Component Analysis). BTW, you guys would have guessed that I am working on Machine Learning.
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SVMs avoid overfitting by choosing a specific hyperplane among the many that can separate the data in the feature space. SVMs find the maximum margin hyperplane, the hyperplane that maximixes the minimum distance from the hyperplane to the closest training point (see Figure 2).
SVM algorithm is quite old - it was developed 1960s, but was extremely popular in 1990s and 2000s. It is a classical (and quite beautiful) part of machine learning courses.
Disadvantages of support vector machine :It does not execute very well when the data set has more sound i.e. target classes are overlapping. In cases where the number of properties for each data point outstrips the number of training data specimens, the support vector machine will underperform.
The standard recommendation for a tutorial in SVMs is A Tutorial on Support Vector Machines for Pattern Recognition by Christopher Burges. Another good place to learn about SVMs is the Machine Learning Course at Stanford (SVMs are covered in lectures 6-8). Both these are quite theoretical and heavy on the maths.
As for source code; SVMLight, libsvm and TinySVM are all open-source, but the code is not very easy to follow. I haven't looked at each of them very closely, but the source for TinySVM is probably the is easiest to understand. There is also a pseudo-code implementation of the SMO algorithm in this paper.
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This is a very good beginner's tutorial on SVM:
SVM explained
I always thought StompChicken's recommended tutorial was a bit confusing in the way that they jump right into talking about bounds and VC statistics and trying to find the optimal machine and such. It's good if you already understand the basics, though.
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Lots of video lectures on SVM:
http://videolectures.net/Top/Computer_Science/Machine_Learning/Kernel_Methods/Support_Vector_Machines/
I found the one by Colin Campbell to be very useful.
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A practical guide to SVM classification for libsvm
PyML Tutorial for PyML
I think 1 is practical for use, 3 is clear for understanding.
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