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A learning method by stochastic connection weight update
http://hdl.handle.net/2297/6842
http://hdl.handle.net/2297/68420bc0fd0a-3abb-4549-923f-50479f20f7f5
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-NAKAYAMA-K-2036.pdf (495.8 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 learning method by stochastic connection weight update | |||||
| 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× Amakata, Yoshihisa× Nukaga, Ryohei× Nakayama, Kenji |
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| 書誌情報 |
IEEE&INNS, Proc. IJCNN'2001, Washington DC 巻 3, p. 2036-2041, 発行日 2001-07-01 |
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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 | In this paper, we propose a learning method that updates a synaptic weight in probability which is proportional to an output error. Proposed method can reduce computational complexity of learning and at the same time, it can improve the classification ability. We point out that an example produces small output error does not contribute to update of a synaptic weight. As learning progresses, the number of the small error examples will be increasing compared to the big one is decreasing. This unbalance will cause of difficulty of learning large error examples. Proposed method cancels this phenomenon and improve the learning ability. Validity of proposed method is confirmed through 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 | |||||
