Abstract
In this study, we extend a minimal resource-allocating network (MRAN) which is an on-line learning system for Gaussian radial basis function networks (GRBFs) with growing and pruning strategies so as to realize dimension selection and low computational complexity. We demonstrate that the proposed algorithm outperforms conventional algorithms in terms of both accuracy and computational complexity via some experiments.
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© 2004 Springer-Verlag Berlin Heidelberg
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Nishida, K., Yamauchi, K., Omori, T. (2004). An On-Line Learning Algorithm with Dimension Selection Using Minimal Hyper Basis Function Networks. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_77
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DOI: https://doi.org/10.1007/978-3-540-30499-9_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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