誤差逆伝播学習法による自己組織化ロバスト主成分分析
Use this link to cite this item : https://ir.lib.hiroshima-u.ac.jp/00017770
| ID | 17770 |
| file | |
| title alternative | Self-Organized Robust Principal Component Analysis by Back-Propagation Learning
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| creator |
Higuchi, Isao
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| subject | robust principal component analysis
neural networks
back-propagation
self-organizing rule
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| NDC |
Technology. Engineering
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| abstract | The purpose of this study is the suggestion of a self-organized back-propagation algorithm for robust principal component analysis. The self-organizing algorithm that discriminates the influence of data automatically is applied to learning of a sandglass type neural network.
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| journal title |
Graduate School of Engineering, Hiroshima University
|
| volume | Volume 53
|
| issue | Issue 1
|
| start page | 1
|
| end page | 4
|
| date of issued | 2004-12
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| publisher | 広島大学大学院工学研究科
国立情報学研究所
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| issn | 1347-7218
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| ncid | |
| language |
jpn
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| nii type |
Departmental Bulletin Paper
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| HU type |
Departmental Bulletin Papers
|
| DCMI type | text
|
| format | application/pdf
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| text version | publisher
|
| department |
Graduate School of Engineering
|
| 他の一覧 |
