Automatic Generation of GRBF Networks Using the IntegralWavelet

Learning can often be viewed as the problem of mapping from an input space to an output space. Examples of these mappings are used to construct a continuous function that approximates given data and generalizes for intermediate instances. Generalized Radial Basis Function (GRBF) networks are used to formulate this approximating function. A novel method is introduced that uses the Integral Wavelet Transform to construct an optimal GRBF network for a given mapping and error bound. Simple one-dimensional examples are used to demonstrate how the optimal network is superior to one constructed using standard ad hoc optimization techniques. The paper concludes with an application of optimal GRBF networks to a multidimensional problem (15 ? 20 dimensions), real-time object recognition and pose estimation. The results of this application are favorable and the optimal GRBF network outperforms a GRBF network constructed using a traditional method.