@Override public Model learn(ExampleSet exampleSet) throws OperatorException {
  Parameter params=getParameters(exampleSet);
  if (exampleSet.size() < 2) {
    throw new UserError(this,110,2);
  }
  Linear.resetRandom();
  Linear.disableDebugOutput();
  Problem problem=getProblem(exampleSet);
  de.bwaldvogel.liblinear.Model model=Linear.train(problem,params);
  return new FastMarginModel(exampleSet,model,getParameterAsBoolean(PARAMETER_USE_BIAS));
}

@ Override public Model learn ( ExampleSet exampleSet ) throws OperatorException { Parameter params = getParameters ( exampleSet ) ; if ( exampleSet . size ( ) < 2 ) { throw new UserError ( this , 110 , 2 ) ; } Linear . resetRandom ( ) ; Linear . disableDebugOutput ( ) ; Problem problem = getProblem ( exampleSet ) ; de . bwaldvogel . liblinear . Model model = Linear . train ( problem , params ) ; return new FastMarginModel ( exampleSet , model , getParameterAsBoolean ( PARAMETER_USE_BIAS ) ) ; }
learns a new svm model with the libsvm package .
