Noise injection into inputs in sparsely connected Hopfield and winner-take-all neural networks

Noise injection into inputs in sparsely connected Hopfield and winner-take-all neural networks

In this paper, we show that noise injection into inputs in unsupervised learning neural networks does not improve their performance as it does in supervised learning neural networks. Specifically, we show that training noise degrades the classification ability of a sparsely connected version of the...

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書目詳細資料
主要作者:	Wang, Lipo.
其他作者:	School of Electrical and Electronic Engineering
格式:	Article
語言:	English
出版:	2012
主題:	DRNTU::Engineering::Electrical and electronic engineering
在線閱讀:	https://hdl.handle.net/10356/94091 http://hdl.handle.net/10220/8195
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

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