We solve the porblem of Support vector classification using both the primal and dual forms of the problem. We also analyze the VC dimension of the problem and the generalization error bound. Both formulations are complete even for the kernelized versions of Support Vector machines.
We test by training a simple spam classifier using our convex opimization method.
The final report is Group_32_Report.pdf