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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). Specifically, the proposed algorithm utilizes the correntropy induced metric (CIM) for defining a similarity measur...

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書誌詳細
主要な著者:	Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping
フォーマット:	論文
出版事項:	Institute of Electrical and Electronics Engineers 2019
主題:	QA75 Electronic computers. Computer science
オンライン･アクセス:	http://eprints.um.edu.my/24041/ https://doi.org/10.1109/ACCESS.2019.2921832
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

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