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

Figure 4

The learning and classification comparison of the HSARBF-ELM network classifier based on the original training set and kernel holistic learning and division in the DM dataset, where the number of original training sets is 100 and the initial kernel width is 0.1. (a) Learning the original training set to generate different RBF kernels. (b) Further learning and screening on the basis of each RBF kernel to generate new sample vectors. (c) Classification effect obtained by learning the parameters of the classifier using the original training set. (d) Classification effect obtained by merging the original sample set with the newly generated sample set and learning the classifier parameters.

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