Chung-Chih Chen

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It is easy for a multi-layered perception (MLP) to form open plane classification borders, and for a radial basis function network (RBFN) to form closed circular or elliptic classification borders. In contrast, it is difficult for a MLP to form closed circular or elliptic classification borders, and for RBFN to form open plane classification borders. Hence,(More)
Radial basis function network (RBFN), commonly used in the classification applications, has two parameters, kernel center and radius that can be determined by unsupervised or supervised learning. But it has a disadvantage that it considers that all the independent variables have the equal weights. In that case, the contour lines of the kernel function are(More)
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