Generalization Properties of Radial Basis Functions

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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Sherif Botros, Christopher Atkeson


We examine the ability of radial basis functions (RBFs) to generalize. We compare the performance of several types of RBFs. We use the inverse dy(cid:173) namics of an idealized two-joint arm as a test case. We find that without a proper choice of a norm for the inputs, RBFs have poor generalization properties. A simple global scaling of the input variables greatly improves performance. We suggest some efficient methods to approximate this dis(cid:173) tance metric.