ステップ型の基底関数を用いた回帰モデルの統計的性質について On the Statistical Properties of Regression Model Using Step-Type Basis Functions

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One of the differences between the regression models using the function representation of 3-layered neural network and the traditional linear regression models is whether the nonlinear parameters associated with the basis functions exist or not, where these parameters play a role of varying the form of the basis so as to minimize the square error. In this study, we gave attention to this feature and defined the regression model using the function representation with step-type discrete variable basis. Then we obtained the bounds of the asymptotic expectations of the least square error and the prediction square error with respect to the sample distribution using the extreme value theory. These results will provide an effective approach to the statistical properties of 3-layered neural network.

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 4(2), 74-82, 1997-06-05

    Japanese Neural Network Society

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各種コード

  • NII論文ID(NAID)
    10008841314
  • 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  J-STAGE 
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