Learning errors by radial basis function neural networks and regularization networks

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作者：
Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod vodárenskou věžíí 2, Prague 8, Czech Republic [1 ]
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来源：
Int. J. Grid Distrib. Comput. | 2009年 / 1卷 / 49-58期
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Functions - Learning algorithms - Computation theory;
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摘要：
Regularization theory presents a sound framework to solving supervised learning problems. However, there is a gap between the theoretical results and practical suitability of regularization networks (RN). Radial basis function networks (RBF) that can be seen as a special case of regularization networks have a rich selection of learning algorithms. In this work we study a relationship between RN and RBF, and show that theoretical estimates for RN hold for a concrete RBF applied to real-world data, to a certain degree. This can provide several recommendations for strategies on choosing number of units in RBF network.
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