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International Conference on Computational Intelligence and Neuroscience(ICCIN2002), 2002
This paper proposes a topographic kernel-based regression method in which kernel bases are self-organized by the Kohonen's Self-Organizing Feature Map (SOM). By the clustering of the training samples using SOM, the number of kernel bases is restricted to the specified small value. It is also expected that the generalization ability of the network is improved by introducing the neighboring relations between the kernel bases. We also employ a top-down learning to modify the location of the bases to minimize the mean squared error because the location of the bases self-organized by SOM are not supposed to be optimum for the given regression task. Experimental results shows that the location of the kernel bases is automatically self-organized depending on both the local densities of the training samples and the local complexities of the target function.
