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Title: Noise injection into inputs in sparsely connected Hopfield and winner-take-all neural networks
Authors: Wang, Lipo.
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 1997
Source: Wang, L. (1997). Noise injection into inputs in sparsely connected Hopfield and winner-take-all neural networks. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 27(5), 868-870.
Series/Report no.: IEEE transactions on systems, man, and cybernetics – Part B: cybernetics
Abstract: 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 Hopfield neural network, whereas the performance of a sparsely connected winner-take-all neural network does not depend on the injected training noise.
DOI: 10.1109/3477.623239
Rights: © 1997 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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