newVector.addElement(new Option("\tFeature-space normalization (only  non-linear for polynomial kernels).",
newVector.addElement(new Option("\tFeature-space normalization (only for non-linear polynomial kernels).",
  public Enumeration listOptions() {    Vector newVector = new Vector(13);    newVector.addElement(new Option("\tThe complexity constant C. (default 1)",				    "C", 1, "-C <double>"));    newVector.addElement(new Option("\tThe exponent for the "				    + "polynomial kernel. (default 1)",				    "E", 1, "-E <double>"));    newVector.addElement(new Option("\tGamma for the "				    + "RBF kernel. (default 0.01)",				    "G", 1, "-G <double>"));    newVector.addElement(new Option("\tWhether to 0=normalize/1=standardize/2=neither. " +				    "(default 0=normalize)",				    "N", 1, "-N"));    newVector.addElement(new Option("\tFeature-space normalization (only  non-linear for polynomial kernels).",				    "F", 0, "-F"));    newVector.addElement(new Option("\tUse lower-order terms (only for non-linear polynomial kernels).",				    "O", 0, "-O"));    newVector.addElement(new Option("\tUse RBF kernel. " +    				    "(default poly)",				    "R", 0, "-R"));    newVector.addElement(new Option("\tThe size of the kernel cache. " +				    "(default 1000003)",				    "A", 1, "-A <int>"));    newVector.addElement(new Option("\tThe tolerance parameter. " +				    "(default 1.0e-3)",				    "T", 1, "-T <double>"));    newVector.addElement(new Option("\tThe epsilon for round-off error. " +				    "(default 1.0e-12)",				    "P", 1, "-P <double>"));    newVector.addElement(new Option("\tFit logistic models to SVM outputs. ",				    "M", 0, "-M"));    newVector.addElement(new Option("\tThe number of folds for the internal cross-validation. " +				    "(default -1, use training data)",				    "V", 1, "-V <double>"));    newVector.addElement(new Option("\tThe random number seed for the internal cross-validation. " +				    "(default 1)",				    "W", 1, "-W <double>"));    return newVector.elements();  }