The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Universal Approximation Capability of Broad Learning System and Its Structural Variations
Abstract
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.
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Cited by
GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition
Zhang, T., Wang, X., Xu, X., & Chen, C. L. P. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2937768 · Cited by 396
Self-citationIncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection
Liu, L., Kuang, Z., Chen, Y., Xue, J.-H., Yang, W., & Zhang, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3002583 · Cited by 90
Metadata
- Authors
- Chen, C. L. P., Liu, Z., & Feng, S.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2019-04
- Volume
- 30
- Issue
- 4
- Pages
- 1191-1204
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2890126432
- Cited by
- 549
- References
- 35
- FWCI
- 28.01
- Type
- journal article
- Language
- en
- Open access
- closed
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