進化型自己組織化マップにおけるノード領域の自動調整法の検討-広島市立大学機関リポジトリ

In this study, the method of classifying data or extracting the regularity of the data is proposed in order to acquire useful information from data. We pay our attention to clustering and visualization. We improve Evolving Self-organizing maps (ESOM) which is one of the clustering techniques proposed by Kasabov. Since the node area is always constant in ESOM, there is a problem that a parameter for node area must be determined for each data set. Therefore we propose a method to automatically adjust the parameter from the quantization error of the node to solve this problem.

掲載雑誌名

2012 IEEE SMC Hiroshima Chapter Young Researchers' Workshop Proceedings = 2012 IEEE SMC Hiroshima Chapter 若手研究会講演論文集

開始ページ

終了ページ

102

出版年月日

2012-07

出版者

IEEE SMC Hiroshima Chapter

本文言語

日本語

資料タイプ

会議発表論文

著者版フラグ

著者版

権利情報

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