/** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ 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 (aka 1-vs-1).\n\n" + "To obtain proper probability estimates, use the option that fits " + "calibration 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" + getTechnicalInformation().toString(); }
/** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ 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 (aka 1-vs-1).\n\n" + "To obtain proper probability estimates, use the option that fits " + "calibration 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" + getTechnicalInformation().toString(); }