Abstract
We present a new constructive algorithm for building Radial-Basis-Function (RBF) network classifiers and a tree based associated algorithm for fast processing of the network. This method, named Constructive Tree Radial-Basis-Function (CTRBF), allows to build and train a RBF network in one pass over the training data set. The training can be in supervised or unsupervised mode. Furthermore, the algorithm is not restricted to fixed input size problems. Several construction and pruning strategies are discussed. We tested and compared this algorithm with classical RBF and multilayer perceptrons on a real world problem: on-line handwritten character recognition. While instantaneous incremental learning is the major property of the architecture, the tree associated to the RBF network gives impressive speed improvement with minimal performance losses. Speed-up factors of 20 over classical RBF have been obtained.
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© 1993 Springer-Verlag Berlin Heidelberg
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Gentric, P., Withagen, H.C.A.M. (1993). Constructive methods for a new classifier based on a radial-basis-function neural network accelerated by a tree. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_135
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DOI: https://doi.org/10.1007/3-540-56798-4_135
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