Abstract
Incremental Net Pro (IncNet Pro) with local learning feature and statistically controlled growing and pruning of the network is introduced. The architecture of the net is based on RBF networks. Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm. IncNet Pro is similar to the Resource Allocation Network described by Platt in the main idea of the expanding the network. The statistical novel criterion is used to determine the growing point. The Bi-radial functions are used instead of radial basis functions to obtain more flexible network.
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© 1997 Springer-Verlag Berlin Heidelberg
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Jankowski, N., Kadirkamanathan, V. (1997). Statistical control of RBF-like networks for classification. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020185
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DOI: https://doi.org/10.1007/BFb0020185
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