+"\t0=none, 1=log liklihood (default)n",
+"\t0=none, 1=log likelihood (default)",
  public Enumeration listOptions() {    Vector newVector = new Vector(9);    newVector.addElement(new Option(	      "\tTurn on debugging output.",	      "D", 0, "-D"));    newVector.addElement(new Option(	      "\tMinimum size of chunks.\n"	      +"\t(default 500)",	      "C", 1, "-C <num>"));    newVector.addElement(new Option(	      "\tMaximum size of chunks.\n"	      +"\t(default 20000)",	      "M", 1, "-M <num>"));    newVector.addElement(new Option(	      "\tSize of validation set.\n"	      +"\t(default 5000)",	      "V", 1, "-V <num>"));    newVector.addElement(new Option(	      "\tFull name of 'weak' learner to boost.\n"	      +"\teg: weka.classifiers.DecisionStump",	      "W", 1, "-W <learner class name>"));    newVector.addElement(new Option(	      "\tCommittee pruning to perform.\n"	      +"\t0=none, 1=log liklihood (default)n",	      "P", 1, "-P <pruning type>"));    newVector.addElement(new Option(	      "\tUse resampling for boosting.",	      "Q", 0, "-Q"));    newVector.addElement(new Option(	      "\tSeed for resampling. (Default 1)",	      "S", 1, "-S <num>"));    if ((m_Classifier != null) &&	(m_Classifier instanceof OptionHandler)) {      newVector.addElement(new Option(	  "",	  "", 0, "\nOptions specific to weak learner "	  + m_Classifier.getClass().getName() + ":"));      Enumeration enum = ((OptionHandler)m_Classifier).listOptions();      while (enum.hasMoreElements()) {	newVector.addElement(enum.nextElement());      }    }