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The effects of quantization on the backpropagation learning
http://hdl.handle.net/2297/6784
http://hdl.handle.net/2297/6784dcb37160-f831-42f9-9f40-1245d46889bb
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-NAKAYAMA-K-1896.pdf (400.3 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 | The effects of quantization on the backpropagation learning | |||||
| 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 |
Ikeda, Kazushi
× Ikeda, Kazushi× Suzuki, Akihiro× Nakayama, Kenji |
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| 書誌情報 |
IEEE International Conference on Neural Networks - Conference Proceedings p. 1896-1900, 発行日 1997-06-01 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1098-7576 | |||||
| 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 | The effects of the quantization of the parameters of a learning machine are discussed. The learning coefficient should be as small as possible for a better estimate of parameters. On the other hand, when the parameters are quantized, it should be relatively larger in order to avoid the paralysis of learning originated from the quantization. How to choose the learning coefficient is given in this paper from the statistical point of view. | |||||
| 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 | |||||
