+ "Methods - Support Vector Learning, B. Schlkopf, C. Burges, and "
+ "Methods - Support Vector Learning, B. Schoelkopf, C. Burges, and "
  public String globalInfo() {    return  "Implements John Platt's sequential minimal optimization "      + "algorithm for training a support vector classifier.\n\n"      + "This implementation globally replaces all missing values and "      + "transforms nominal attributes into binary ones. It also "      + "normalizes all attributes by default. (In that case the coefficients "      + "in the output are based on the normalized data, not the "      + "original data --- this is important for interpreting the classifier.)\n\n"      + "Multi-class problems are solved using pairwise classification.\n\n"      + "To obtain proper probability estimates, use the option that fits "      + "logistic regression models to the outputs of the support vector "      + "machine. In the multi-class case the predicted probabilities "      + "are coupled using Hastie and Tibshirani's pairwise coupling "      + "method.\n\n"      + "Note: for improved speed normalization should be turned off when "      + "operating on SparseInstances.\n\n"      + "For more information on the SMO algorithm, see\n\n"      + "J. Platt (1998). \"Fast Training of Support Vector "      + "Machines using Sequential Minimal Optimization\". Advances in Kernel "      + "Methods - Support Vector Learning, B. Schlkopf, C. Burges, and "      + "A. Smola, eds., MIT Press. \n\n"      + "S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy,  "      + "\"Improvements to Platt's SMO Algorithm for SVM Classifier Design\".  "      + "Neural Computation, 13(3), pp 637-649, 2001.";  }