多重マップモデルによる2種の情報の分離抽出 Separate Extraction of Tow Kind of Information by Self-Organizing-Overlapping-Map

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著者

    • 紀田 馨 KIDA Kaoru
    • 大阪大学大学院基礎工学研究科システム人間系 Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University
    • 和田 浩司 WADA Koji
    • 大阪大学基礎工学部生物工学科 Department of Biophysical Engineering, Faculty of Engineering Science, Osaka University
    • 倉田 耕治 KURATA Koji
    • 大阪大学大学院基礎工学研究科システム人間系 Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University

抄録

Self-organizing continuously-overlapping map is shown to have ability to detect the first and second nonlinear principal components. This is an extended version of the self-organizing overlapping mapping. The model was applied to FFT data of sound, and some others. These data are characterized by a combination of two kinds of features, such as the pitch and the quality of tone. The model has two self-organizing layers. One layer extracts and maps continuously one feature, and the other layer does the same with respect to the other feature. The ability of generalization depending on data structure is demonstrated. Comparison to Kohonen's SOM is also discussed.

収録刊行物

  • 日本神経回路学会誌 = The Brain & neural networks

    日本神経回路学会誌 = The Brain & neural networks 6(4), 196-202, 1999-12-05

    Japanese Neural Network Society

参考文献:  7件中 1-7件 を表示

被引用文献:  19件中 1-19件 を表示

各種コード

  • NII論文ID(NAID)
    10010424363
  • NII書誌ID(NCID)
    AA11658570
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    1340766X
  • データ提供元
    CJP書誌  CJP引用  J-STAGE 
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