Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)
Eric Chang, Richard P. Lippmann
A new boundary hunting radial basis function (BH-RBF) classifier which allocates RBF centers constructively near class boundaries is described. This classifier creates complex decision boundaries only in regions where confusions occur and corresponding RBF outputs are similar. A predicted square error measure is used to determine how many centers to add and to determine when to stop adding centers. Two experiments are presented which demonstrate the advantages of the BH(cid:173) RBF classifier. One uses artificial data with two classes and two input features where each class contains four clusters but only one cluster is near a decision region boundary. The other uses a large seismic database with seven classes and 14 input features. In both experiments the BH(cid:173) RBF classifier provides a lower error rate with fewer centers than are required by more conventional RBF, Gaussian mixture, or MLP classifiers.