Hi all, To gain better understanding of SVC methods, I am trying to train an SVC and then from the dual coefficients (in the kernel case) and the weights (in the linear case) to calculate rho and to make predictions on new feature vectors. Thus far, I am only successful in the linear case. I have posted some sample code to a paste bin for further clarity [0].
Please help me to understand where I am going wrong. My understanding is that rho, the constant term, should be the same for every support vector. However, in the code, I use the average of all hard-margin support vectors (with an absolute value less than C) to calculate rho. I have compared the sklearn SVC results with the libsvm SVC results. As per the documentation sklearn reports -rho from the libsvm trained SVC. Thanks much, Kevin [0] http://pastebin.com/5fqdh0CV
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