An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

Figure 1

A schematic comparison between the kernel holistic partition and the direct pattern vector classification. (a) Directly partitioning the original sample set. (b) Density clustering of the original sample set and establishing the corresponding RBF kernels to complete the coverage of the original sample space. (c) Filling each subkernel pattern class to establish a new pattern vector to partition the whole kernel. (d) Dividing the original sample and the new filled sample into new sample sets to obtain a new classification surface. (e) Comparing the modified classification surface with the original one.

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