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Universal Approximation Capability of Broad Learning System and Its Structural Variations
文献类型：期刊论文
| 作者 | Chen, C. L. Philip1,2; Liu, Zhulin1; Feng, Shuang1,3 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2019-04-01 |
| 卷号 | 30期号:4页码:1191-1204 |
| 关键词 | Broad learning system (BLS) deep learning face recognition functional link neural networks (FLNNs) non-linear function approximation time-variant big data modeling universal approximation |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2018.2866622 |
| 通讯作者 | Liu, Zhulin(zhulinlau@gmail.com) |
| 英文摘要 | After a very fast and efficient discriminative broad learning system (BLS) that takes advantage of flatted structure and incremental learning has been developed, here, a mathematical proof of the universal approximation property of BLS is provided. In addition, the framework of several BLS variants with their mathematical modeling is given. The variations include cascade, recurrent, and broad-deep combination structures. From the experimental results, the BLS and its variations outperform several exist learning algorithms on regression performance over function approximation, time series prediction, and face recognition databases. In addition, experiments on the extremely challenging data set, such as MS-Celeb-1M, are given. Compared with other convolutional networks, the effectiveness and efficiency of the variants of BLS are demonstrated. |
| WOS关键词 | REGULARIZATION ; RECOGNITION ; ALGORITHM ; NETWORKS ; MACHINE |
| 资助项目 | National Natural Science Foundation of China[61751202] ; National Natural Science Foundation of China[61751205] ; National Natural Science Foundation of China[61572540] ; Macau Science and Technology Development Fund[019/2015/A1] ; Macau Science and Technology Development Fund[079/2017/A2] ; Macau Science and Technology Development Fund[024/2015/AMJ] ; University of Macau |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000461854100017 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Macau Science and Technology Development Fund ; University of Macau |
| 源URL | [http://ir.ia.ac.cn/handle/173211/26823]  |
| 专题 | 离退休人员 |
| 通讯作者 | Liu, Zhulin |
| 作者单位 | 1.Univ Macau, Fac Sci & Technol, Macau 99999, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 3.Beijing Normal Univ, Sch Appl Math, Zhuhai 519087, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, C. L. Philip,Liu, Zhulin,Feng, Shuang. Universal Approximation Capability of Broad Learning System and Its Structural Variations[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(4):1191-1204. |
| APA | Chen, C. L. Philip,Liu, Zhulin,&Feng, Shuang.(2019).Universal Approximation Capability of Broad Learning System and Its Structural Variations.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(4),1191-1204. |
| MLA | Chen, C. L. Philip,et al."Universal Approximation Capability of Broad Learning System and Its Structural Variations".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.4(2019):1191-1204. |
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