title: libsvm scaling of features real and categorical
body: <p>I am wondering if categorical features, after converting to one-hot encoding (e.g. 0 0 0 1 0 0 for 6 possible values of the variable) should be scaled along real features using svm-scale function. libsvm guide apparently says so, I think.</p>
  
  <p>Also, what is the effect on learning in SVM, if there are some features which are undiscriminating, e.g. random noise? Should I remove such features before training? My guess is that these can affect learning because SVM essentially calculates euclidean distances between data points which are represented as vectors of features. I am not much concerned with running time as number of features is small. Please mention standard feature selection algorithm implementation for svm. Any suggestion is welcome. </p>
  
  <p>Thank you.</p>