I'm working through Stanford's computer vision course to refresh some of my math skills (http://vision.stanford.edu/teaching/cs223b/syllabus.html), and I've run into a problem while working with a simple 2d Support Vector Machine.
Basically, the SVM library I am using only returns the vector 'w', which is the normal to the hyperplane (or line in 2d). I have verified that I am getting the correct value. The problem is, it does not contain 'b' in the equation:
w.x + b = 0
How do I compute 'b', so that when I draw the line it correctly bisects my data?
Also, I've noticed that the library I'm using (liblinear for those interested) assumes that the hyperplane bisects the origin (again, it doesn't use 'b'). I believe it assumes that the data is centered about the origin? My question is, can I simply take the average position (avg) and translate all of my data points by avg? Is there a preferred method to prep the data before running it through a SVM?