"\t of estimated entropy.",
"\tof estimated entropy (for equal-width discretization).",
  public Enumeration listOptions() {    Vector newVector = new Vector(7);    newVector.addElement(new Option(              "\tSpecify the (maximum) number of bins to divide numeric"	      + " attributes into.\n"	      + "\t(default class-based discretization)",              "B", 1, "-B <num>"));    newVector.addElement(new Option(              "\tOptimize number of bins using leave-one-out estimate\n"+	      "\t of estimated entropy.",              "O", 0, "-O"));    /* If we decide to implement loading and saving cutfiles like      * the C Discretizer (which is probably not necessary)    newVector.addElement(new Option(              "\tSpecify that the cutpoints should be loaded from a file.",              "L", 1, "-L <file>"));    newVector.addElement(new Option(              "\tSpecify that the chosen cutpoints should be saved to a file.",              "S", 1, "-S <file>"));    */    newVector.addElement(new Option(              "\tSpecify list of columns to Discretize. First"	      + " and last are valid indexes.\n"	      + "\t(default none)",              "R", 1, "-R <col1,col2-col4,...>"));    newVector.addElement(new Option(              "\tInvert matching sense of column indexes.",              "V", 0, "-V"));    newVector.addElement(new Option(              "\tOutput binary attributes for discretized attributes.",              "D", 0, "-D"));    newVector.addElement(new Option(              "\tUse better encoding of split point for MDL.",              "E", 0, "-E"));    newVector.addElement(new Option(              "\tUse Kononenko's MDL criterion.",              "K", 0, "-K"));    return newVector.elements();  }