ナガタ　キヨシ   永田　清   経営学部 経営学科   教授

■ 標題   Decision Making by a Fuzzy Regression Model with Modified Kernel

■ 概要   Regression model is a popular and powerful model for finding a rule from large amount of collected data. In case of nonnumerical data, although fuzzy regression models are proposed and investigated by some researchers, most of them are linear models. In order to construct a non-linear regression model with fuzzy type data set, new type of devices are needed since fuzzy numbers have a complicated behavior in multiplication and division. In this paper, we try to extend a linear fuzzy regression model to non-linear model by adapting a modified kernel method.   Kiyoshi Nagata, Michihiro Amagasa   共著   Proceedings of The Seventh International Conference on Advances in Information Mining and Management   International Academy, Research, and Industry Association   pp.18-23   2017/06

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