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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10445/5281

タイトル: Class-Proximity SOM and its Applications in Classification
著者: Hartono, Pitoyo
Saito, Aya
アブストラクト: In this study, we propose a model of Self-Organizing Map (SOM) capable of mapping highdimensional data into a low dimension space by preserving not only thefeature-proximity of the original data but also their class-proximity. A conventional SOM is known to map original high dimensional data with similar features into points located close to each other in the low dimensional map in a so called competitive layer. In addition to this feature, the proposed SOM is also able to map high dimensional data belonging to a same class in each other's proximities. These characteristics retains the ability of the map to be used as a visualization tool of high dimensional data while also support the execution of high quality pattern classifications in the low dimensional map. In the experiments the classification performance of the proposed SOM is compared to that of MLP with regards to wide varieties of problems.
研究業績種別: 国際会議/International Conference
資料種別: Conference Paper
査読有無: あり/yes
単著共著: 単著/solo
発表雑誌名,発表学会名など: Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (SMC 2008)
開始ページ: 2150
終了ページ: 2155
年月日: 2008年
出版社: IEEE
出現コレクション:ピトヨ・ハルトノ

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