UM
Broad Learning System: Feature extraction based on K-means clustering algorithm
Liu, Zhulin; Zhou, Jin; Chen, C. L. Philip; IEEE
2017
Conference Name2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION, CYBERNETICS AND COMPUTATIONAL SOCIAL SYSTEMS (ICCSS)
Pages683-687
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractBroad Learning System Hi proposed recently demonstrates efficient and effective learning capability. This model is also proved to be suitable for incremental learning algorithms by taking the advantages of random vector flat neural networks. In this paper, a modified BLS structure based on the K-means feature extraction is developed. Compared with the original broad learning system, acceptable performance on more complicated data set, such as CIFAR-10, is achieved. Furthermore, it is proved that the proposed model in Hi is flexible and potential in various applications.
KeywordSingle layer feedforward neural networks SVD random vector functional link networks broad learning system incremental learning deep learning K-means feature representation
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000427352100130
The Source to ArticleWOS
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Liu, Zhulin,Zhou, Jin,Chen, C. L. Philip,et al. Broad Learning System: Feature extraction based on K-means clustering algorithm[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:683-687.
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