Iterative Generation of Chow Parameters Using Nearest Neighbor Relations in Threshold Network

被引:0
作者
Ishii, Naohiro [1 ]
Odagiri, Kazuya [2 ]
Matsuo, Tokuro [1 ]
机构
[1] Adv Inst Ind Technol, Tokyo 1400011, Japan
[2] Sugiyama Jyogakuen Univ, Nagoya, Aichi 4648662, Japan
来源
PROCEEDINGS OF SIXTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICICT 2021), VOL 2 | 2022年 / 236卷
关键词
Nearest neighbor relation; Sequential generation of chow parameters; Boundary vertex; Minimal information of nearest neighbor relation;
D O I
10.1007/978-981-16-2380-6_31
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Intelligent functions and learning are important issues, which are needed in the application fields. Recently, these technologies are extensively studied and developed using threshold neural networks. The nearest neighbor relations are proposed for the basis of the generation of functions and learning. First, the these relations are shown to have minimal information for the discrimination and to be the basis of the inherited information for threshold functions. Second, for the Chow parameter problems, we developed fundamental schemes of the nearest neighbor relations and performed their analysis for the Chow parameters. The sequential generation of the Chow parameters is proposed which is caused by small changes of the connecting weights in threshold neurons.
引用
收藏
页码:357 / 366
页数:10
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