Cited by 268Text and Document Classification TechnologiesTopic ModelingNeural Networks and Applications

Text classification based on deep belief network and softmax regression

Jiang, Mingyang, Liang, Yanchun, Feng, Xiaoyue, Fan, Xiaojing, Pei, Zhili, Xue, Yu, Guan, Renchu·2018·Neural Computing and Applications

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Crossref · 10.1007/s00521-016-2401-x

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43 of 43 references verified0 citation errors · 0 not checkable

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  • A Deep Belief Network (DBN) is rarely used for cyberbullying detection; in the majority of cases, it is used as one of the components of hybrid models.

    “A Deep Belief Network (DBN) is rarely used for cyberbullying detection. In the majority of cases, the network is used as one of the components of hybrid models [126].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [126]

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Authors
Jiang, M., Liang, Y., Feng, X., Fan, X., Pei, Z., Xue, Y., & Guan, R.
Affiliations
  • Inner Mongolia University for Nationalities (CN)
  • Jilin University (CN)
  • Nanjing University of Information Science and Technology (CN)
Corresponding author
Guan, Renchu
Venue
Neural Computing and Applications
Published
2016-06-14
Volume
29
Issue
1
Pages
61-70
ISSN
  • 0941-0643 (ISSN-L)
  • 1433-3058
OpenAlex
W2423124209
Cited by
268
References
43
Citations by year
2026: 2 · 2025: 12 · 2024: 27 · 2023: 29 · 2022: 41 · 2021: 42 · 2020: 54 · 2019: 41 · 2018: 11 · 2017: 8 · 2016: 1
FWCI
16.75
Type
journal article
Language
en
Open access
closed
Locations
Neural Computing and Applications (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
text classification, deep belief network, softmax regression, hybrid model, high-dimensional sparse data, feature extraction, limited-memory BFGS, fine-tuning, Reuters-21578, 20 Newsgroups

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