I'm using SVM-pref (http://svmlight.joachims.org) for a binary classification problem. I don't have much experience with this package and so I seek help with the following questions:
(1) My features are all discrete/nominal. Is there a special way to represent the feature vectors like a special way to convert the nominal values into continuous values or do we just replace the nominal values for dummy numbers like 1, 2, 3 .. etc.?
(2) If the answer to the first question is we replace nominal values with dummy numbers, then my second question is we start numbering feature values from 1 so we have 1:1 but not 1:0 otherwise the learner will consider a zero-value feature as non-existent. Is that correct?
(3) How to we configure the best -c values and the values for the rest of the parameters? Is it only by error and trial or are their other approaches used to decide on these parameters?