Deep belief networks with self-adaptive sparsity
Deep belief networks with self-adaptive sparsity
About this item
Full title
Author / Creator
Qiao, Chen , Yang, Lan , Shi, Yan , Fang, Hanfeng and Kang, Yanmei
Publisher
New York: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Springer US
Subjects
More information
Scope and Contents
Contents
To have the sparsity of deep neural networks is crucial, which can improve the learning ability of them, especially for application to high-dimensional data with small sample size. Commonly used regularization terms for keeping the sparsity of deep neural networks are based on
L
1
-norm or
L
2
-norm; however, they are not the most...
Alternative Titles
Full title
Deep belief networks with self-adaptive sparsity
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2619611932
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2619611932
Other Identifiers
ISSN
0924-669X
E-ISSN
1573-7497
DOI
10.1007/s10489-021-02361-y
