Some Notes of SVM from S-O-F

This is a very good illustration of how SVM-works:

Links from 

<http://stackoverflow.com/questions/3997454/help-me-understand-linear-separability-in-a-binary-svm>

The original is from math.stackexchange.com

The first answer:

As mokus explained, practical support vector machines use a non-linear kernel function to map data into a feature space where they are linearly separable:

Lots of different functions are used for various kinds of data. Note that an extra dimension is added by the transformation.

(Illustration from Chris Thornton, U. Sussex.)

The 2nd answer: YouTube video that illustrates an example of linearly inseparable points that become separable by a plane when mapped to a higher dimension

 "Non-linear classification", with a link tohttp://en.wikipedia.org/wiki/Kernel_trick which explains the technique more generally.

And for SVM, a very good explanation for the equation- hyperplane is:   the w*x + b = 0

language agnostic - Support vector machines - separating hyperplane question - Stack Overflow

http://stackoverflow.com/questions/1530604/support-vector-machines-separating-hyperplane-question/1530737#1530737

It is the equation of a (hyper)plane using a point and normal vector.
Think of the plane as the set of points P such that the vector passing from P0 to P is perpendicular to the normal

Check out these pages for explanation:
http://mathworld.wolfram.com/Plane.html
http://en.wikipedia.org/wiki/Plane_%28geometry%29#Definition_with_a_point_and_a_normal_vector

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