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Interval arithmetic backpropagation
http://hdl.handle.net/2297/6798
http://hdl.handle.net/2297/6798d7138497-7d11-4695-866f-2455e6777455
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-NAKAYAMA-K-1993-375.pdf (211.3 kB)
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | Interval arithmetic backpropagation | |||||
| 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 |
Hernandez, C.A.
× Hernandez, C.A.× Nakayama, Kenji× Fernandez, M. |
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| 書誌情報 |
Proceedings of the International Joint Conference on Neural Mateworks 巻 1, p. 375-378, 発行日 1993-10-01 |
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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 | We present a new extension of the Backpropagation learning algorithm by using interval arithmetic. The proposed algorithm represents a generalization of Backpropagation and contains Backpropagation like a particular case. This new algorithm permits the use of training samples and targets which can be indistinctly points and intervals. Among the possible applications of this algorithm, we report its usefulness to integrate expert's knowledge and experimental samples and also its ability to handle 'don't care attributes' in a simple and natural way in comparison with Backpropagation. It also adds flexibility to the codification of inputs and outputs. | |||||
| 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 | |||||
