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A Training Method with Small Computation for Classification
http://hdl.handle.net/2297/6823
http://hdl.handle.net/2297/6823c3e30f69-6a06-46c0-886e-c90535abfad2
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-NAKAYAMA-K-543.pdf (123.4 kB)
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
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| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | A Training Method with Small Computation for Classification | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| 著者 |
Hara, Kazuyuki
× Hara, Kazuyuki× Nakayama, Kenji |
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| 書誌情報 |
Proceedings of the International Joint Conference on Neural Networks p. III-543-III-548, 発行日 2000-07-01 |
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| 出版者 | ||||||
| 出版者 | IEEE(Institute of Electrical and Electronics Engineers) | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | A training data selection method for multi-class data is proposed. This method can be used for multilayer neural networks (MLNN). The MLNN can be applied to pattern classification, signal process, and other problems that can be considered as the classification problem. The proposed data selection algorithm selects the important data to achieve a good classification performance. However, the training using the selected data converges slowly, so we also propose an acceleration method. The proposed training method adds the randomly selected data to the boundary data. The validity of the proposed methods is confirmed through the computer simulation. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
