I like to find the weight vector for input-space features in a structured SVM. The idea is to identify the most important set of input-space features (based on the magnitude of their corresponding weights). I know that in a binary SVM the weight vector can be written as a linear combination of examples, and the magnitude of those weights represents how much they were effective for the prediction problem at hand. But how do you compute the same for an SSVM?
feature weights in structured support vector machine
svm