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A Multilayer Neural Network with Nonlinear Inputs and Trainable Activation Functions: Structure and Simultaneous Learning Algorithm
http://hdl.handle.net/2297/6838
http://hdl.handle.net/2297/68387da04f89-8518-40da-952a-6bc9604df313
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-NAKAYAMA-K-1657.pdf (445.9 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 | A Multilayer Neural Network with Nonlinear Inputs and Trainable Activation Functions: Structure and Simultaneous Learning Algorithm | |||||
| 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 |
Nakayama, Kenji
× Nakayama, Kenji× Hirano, Akihiro× Ido, Issei |
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| 書誌情報 |
Proceedings of the International Joint Conference on Neural Networks 巻 3, p. 1657-1661, 発行日 1999-07-01 |
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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 | Network size of neural networks is highly dependent on activation functions. A trainable activation function has been proposed, which consists of a linear combination of some basic functions. The activation functions and the connection weights are simultaneously trained. An 8 bit parity problem can be solved by using a single output unit and no hidden unit. In this paper, we expand this model to multilayer neural networks. Furthermore, nonlinear functions are used at the unit inputs in order to realize more flexible transfer functions. The previous activation functions and the new nonlinear functions are also simultaneously trained. More complex pattern classification problems can be solved with a small number of units and fast convergence. | |||||
| 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 | |||||
