how to classify colors using weka jar in java

May output additional info to the console -do-not-check-capabilities If set, classifier is run in debug mode and (default: .PolyKernel) -calibrator įull name of calibration model, followed by options. (default 1.0e-12) -Mįit calibration models to SVM outputs. Whether to 0=normalize/1=standardize/2=neither. Finally, it also assumes that no instance has Off also means that no header information will be stored if the

Turning them off assumes that data is purely numeric, doesn'tĬontain any missing values, and has a nominal class. In: Advances in Neural Information Processing Systems, = , Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998.

Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. Note: for improved speed normalization should be turned off when operating on SparseInstances.įor more information on the SMO algorithm, see In the multi-class case, the predicted probabilities are coupled using Hastie and Tibshirani's pairwise coupling method. To obtain proper probability estimates, use the option that fits calibration models to the outputs of the support vector machine. Multi-class problems are solved using pairwise classification (aka 1-vs-1). (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.) It also normalizes all attributes by default. This implementation globally replaces all missing values and transforms nominal attributes into binary ones. Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

How to classify colors using weka jar in java