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Zhenghua Huang
Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
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1 Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
2 Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing 10008, China
3 Graduate School, Northern Arizona University, Flagstaff, AZ 86011, USA
Received 1970-01-01; Accepted 1970-01-01; Issue published 1970-01-01
The Principle of Homology Continuity (PHC) based covering learning method is an effective method to solve the pattern recognition problem. However, PHC and the existence of optimal coverage are not mathematical proven. To address this issue, we firstly give the mathematical description and theoretical proof of PHC. On this basis, the theoretical definition of optimal coverage is introduced. Optimal coverage can determine the internal connections among samples as prior knowledge and use covering neurons to learn prior knowledge. Finally, we propose a kind of covering neuron model, and the effectiveness of which is demonstrated through extensive experiments conducted on the CIFAR-10, LFW, and YTF datasets.
Covering neuron models
Deep learning
Homology continuity
Optimal coverage
Cite This Article
N. Xin, W. Yuebao, T. Weijuan, L. Liang, C. Weiwei, and H. Zhenghua, “A Biomimetic Covering Learning Method Based on Principle of Homology Continuity”, Issue 1, vol. 1, no. 1, pp. 1–13, 2021, doi: TPRIS.2022.7JCBV4VU.

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