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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- 黒木 秀一 KUROGI Shuichi
- 九州工業大学大学院 工学研究院 Faculty of Engineering Kyushu Institute of Technology
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Author(s)
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- 黒木 秀一 KUROGI Shuichi
- 九州工業大学大学院 工学研究院 Faculty of Engineering Kyushu Institute of Technology
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
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- The Brain & Neural Networks
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The Brain & Neural Networks 16(2), 81-92, 2009-06-05
Japanese Neural Network Society
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