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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
2015
Source PublicationIEEE Transactions on Fuzzy Systems
ISSN10636706
Volume23Issue:6Pages:2163
Abstract

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)
DOI10.1109/TFUZZ.2015.2406889
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000365989300021
The Source to ArticleScopus
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Cited Times [WOS]:70   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
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.
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