Abstract
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 reduction of data variables and classification method based on the nearest neighbor relations for threshold networks. First, the nearest neighbor relations are shown to be useful for the generation of threshold functions and Chow parameters. Second, the extended application of the nearest neighbor relations is developed for the reduction of variables based on convex cones. The edges of convex cones are compared for the reduction of variables. Further, hyperplanes with reduced variables are obtained on the convex cones for data classification.
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Ishii, N., Iwata, K., Odagiri, K., Nakashima, T., Matsuo, T. (2020). Reduction of Variables and Generation of Functions Through Nearest Neighbor Relations in Threshold Networks. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_45
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DOI: https://doi.org/10.1007/978-3-030-48791-1_45
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