区域渐近增强与感受野结合的多核近似学习网

doi:10.3969/j.issn.1000-1158.2021.06.02

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Abstract In order to fully extract the identifiable information in the image and improve classification accuracy rate, a structure named multiple kernel empirical learning network(MKELN) was proposed. In the feature extraction part, the original image is gradually enhanced by the two-dimensional Gaussian distribution. The local receptive field and the global receptive field are used to fully extract features in the original image and the gradually regional enhancement image, and they are connected in series to form feature vector that represents an image. In the classification part, a multiple kernel empirical algorithm was proposed, and the low rank feature matrix is used as the hidden layer of the network to solve the output weight of the network. To verify the effectiveness of this network, it was tested with USPS, MNIST and NORB data sets. The experiment proves that the proposed MKELN can further extract feature information based on ELM-LRF, effectively improving the classification accuracy.

Key words： metrology image recognition multiple kernel empirical mapping two-dimensional Gaussian distribution local receptive field global receptive field extreme learning machine

Received: 20 July 2019 Published: 23 June 2021

PACS:	TB96
	TB973

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	Articles by authors
	LIU Bin
	LIU Jing
	WU Chao
	LI Ya-qian
	ZHANG Ya-ru
	YANG You-heng

Cite this article:

LIU Bin,LIU Jing,WU Chao, et al. Multiple Kernel Empirical Learning Network with Gradually Enhanced Region and Receptive Field[J]. Acta Metrologica Sinica, 2021, 42(6): 694-703.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.06.02 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I6/694

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