Training and evaluation of SVM predictors. The circle and pies indicate the dataset and portions thereof. The procedure in each dashed box was repeated ten times. The whole dataset was randomly divided into ten parts, with nine parts combined to construct the SVM model, and the remaining one to evaluate the model. The combined data for model construction were further divided randomly into ten subsets, in which nine subsets were combined to serve as training data, and the 10th subset served as test data. See text for details.