バグサイズを可変とするバグ外推定による汎化能力向上  [in Japanese] Improving Generalization Performance Via Out-of-Bag Estimate Using Variable Size of Bags  [in Japanese]

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Author(s)

Abstract

This paper describes a method for improving the generalization performance by means of the out-of-bag estimate for the generalization error in regression problems. We analyze the effect of the size of bags from the viewpoint of piecewise linear prediction achieved by the CAN2 (competitive associative net). Here, the CAN2 basically is a neural net for learning efficient piecewise linear approximation of nonlinear functions. We also examine and validate the effectiveness via numerical experiments.

Journal

  • The Brain & Neural Networks

    The Brain & Neural Networks 16(2), 81-92, 2009-06-05

    Japanese Neural Network Society

References:  22

Cited by:  1

Codes

  • NII Article ID (NAID)
    10025185438
  • NII NACSIS-CAT ID (NCID)
    AA11658570
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    1340766X
  • Data Source
    CJP  CJPref  IR  J-STAGE 
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