New journal article: Strongly Agree or Strongly Disagree? : Rating Features in Support Vector Machines

In February 2016 CBS Professor Dolores Romero Morales and colleagues worked out a new peer-reviewed journal article issued in Information Sciences. The journal article - Strongly Agree or Strongly Disagree? : Rating Features in Support Vector Machines - discusses the following: Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, defined by the so-called feature rating levels.

You can find the journal article Strongly Agree or Strongly Disagree? : Rating Features in Support Vector Machines here.