| Reduction of data variables is an important issue and it is needed for the processing of higher dimensional data in the application domains and AI, in which threshold neural networks are extensively used. We develop a reduc-tion of data variables and classification method based on the nearest neigh-bor relations for threshold networks. First, the nearest neighbor relations are shown to be useful for the generation of threshold functions and Chow pa-rameters. Second, the extended application of the nearest neighbor relations is developed for the reduction of variables based on convex cones. The edg-es of convex cones are compared for the reduction of variables. Further, hy-perplanes with reduced variables are obtained on the convex cones for data classification. |
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