To measure the region contained in the decision boundary, we employ a Monte Carlo (MC) procedure, where we (1) generate 5000 uniform-randomly distributed response vectors within a sphere centered at the center of the training data and with radius extending to the outermost training vector then (2) count the fraction of these responses that fall inside the decision boundary (and, hence, are classified as normal). A smaller fraction indicates the area within the decision boundary is small. Fig. illustrates.