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- Tomimori Haruka
- Graduate School of Information, Production and Systems, Waseda University
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- Chen Kui-Ting
- Research Center of Information, Production and Systems, Waseda University
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- Baba Takaaki
- Graduate School of Information, Production and Systems, Waseda University
説明
Nowadays, a convolutional neural network (CNN) is considered as a deep learning method for image and voice recognition. A CNN can achieve higher recognition accuracy than other approaches since it can automatically extract features by its learning procedure. However, the training procedure of a CNN is time-consuming. Since the functions of a CNN are close to those of a human brain, when a CNN is applied to a complex application, it must be trained by a large amount of training data, resulting in the size of the CNN becoming huge. To train such a huge neural network by computers, a tremendous amount of training time is required. In this paper, an efficient approach is proposed that can markedly reduce the training time while only slightly sacrificing the recognition accuracy of the training procedure.
収録刊行物
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- 信号処理
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信号処理 21 (4), 155-158, 2017
信号処理学会
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詳細情報 詳細情報について
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- CRID
- 1390282679440404736
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- NII論文ID
- 130005815303
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- ISSN
- 18801013
- 13426230
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可