Efficient initialization scheme for the self-organizing feature map algorithm

Mu Chun Su; Ta Kang Liu; Hsiao Te Chang

Efficient initialization scheme for the self-organizing feature map algorithm

Mu Chun Su, Ta Kang Liu, Hsiao Te Chang

資訊工程學系

研究成果: 會議貢獻類型 › 會議論文 › 同行評審

24 引文斯高帕斯（Scopus）

摘要

It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters of the algorithm, namely, the learning-rate parameter and the neighborhood function. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Two data sets are tested to illustrate the performance of the proposed method.

原文	???core.languages.en_GB???
頁面	1906-1910
頁數	5
出版狀態	已出版 - 1999
事件	International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA 持續時間: 10 7月 1999 → 16 7月 1999

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Joint Conference on Neural Networks (IJCNN'99)
城市	Washington, DC, USA
期間	10/07/99 → 16/07/99

引用此

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AU - Liu, Ta Kang

AU - Chang, Hsiao Te

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AB - It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters of the algorithm, namely, the learning-rate parameter and the neighborhood function. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Two data sets are tested to illustrate the performance of the proposed method.

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Efficient initialization scheme for the self-organizing feature map algorithm

摘要

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