Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning
Chen C.L.P.1; Zhang C.-Y.1; Chen L.1; Gan M.2
Source PublicationIEEE Transactions on Fuzzy Systems

In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. For regular RBM, the relationships between visible units and hidden units are restricted to be constants. This restriction will certainly downgrade the representation capability of the RBM. To avoid this flaw and enhance deep learning capability, the fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers. This way, the original RBM becomes a special case in the FRBM, when there is no fuzziness in the FRBM model. In the process of learning FRBM, the fuzzy free energy function is defuzzified before the probability is defined. The experimental results based on bar-and-stripe benchmark inpainting and MNIST handwritten digits classification problems show that the representation capability of FRBM model is significantly better than the traditional RBM. Additionally, the FRBM also reveals better robustness property compared with RBM when the training data are contaminated by noises. © 2015 IEEE.

KeywordDeep Learning Fuzzy Deep Networks Fuzzy Restricted Boltzmannmachine Image Classification Image Inpainting Restricted Boltzmann Machine (Rbm)
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000365989300021
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:109   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Affiliation1.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 999078, Peoples R China
2.Hefei Univ Technol, Dept Comp & Informat Sci, Hefei 230009, Peoples R China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Chen C.L.P.,Zhang C.-Y.,Chen L.,et al. Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning[J]. IEEE Transactions on Fuzzy Systems,2015,23(6):2163.
APA Chen C.L.P.,Zhang C.-Y.,Chen L.,&Gan M..(2015).Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning.IEEE Transactions on Fuzzy Systems,23(6),2163.
MLA Chen C.L.P.,et al."Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning".IEEE Transactions on Fuzzy Systems 23.6(2015):2163.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chen C.L.P.]'s Articles
[Zhang C.-Y.]'s Articles
[Chen L.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen C.L.P.]'s Articles
[Zhang C.-Y.]'s Articles
[Chen L.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen C.L.P.]'s Articles
[Zhang C.-Y.]'s Articles
[Chen L.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.