I used the MATLAB interface of libsvm for doing binary classification of 997-dimensional training data. I am trying to understand how the resulting model is used to compute the predicted output (which we get by calling
The model contains fields (it has linear kernel):
nSV = [546; 246]; totalSV=792; rho = 0.093 and svCoeff [792x1 double] and SVs [792x997 double]
I thought that we must be simply multiplying svCoeff with SVs to get a [997x1] matrix which we then multiply with the actual feature, before shareholding by rho. But that's not the case. Can someone illustrate with a simple equation how these parameters are used to do classification?