L1/2 regularization learning for smoothing interval neural networks: Algorithms and convergence analysis

Interval neural networks can easily address uncertain information, since they are capable of handling various kinds of uncertainties inherently which are represented by interval. Lq (0 < q < 1) regularization was proposed after L1 regularization for better solution of sparsity problems, among...

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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 272; pp. 122 - 129
Main Authors:	Yang, Dakun, Liu, Yan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 10.01.2018
Subjects:	Batch gradient algorithm Convergence Interval computation Interval neural network Smoothing L1/2 regularization Batch gradient algorithm Interval computation Interval neural network Smoothing L1/2 regularization Convergence
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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