An effective classifier based on convolutional neural network and regularized extreme learning machine

Chunmei He; Hongyu Kang; Tong Yao; Xiaorui Li; Chunmei He; Hongyu Kang; Tong Yao; Xiaorui Li

doi:10.3934/mbe.2019420

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 8309-8321. doi: 10.3934/mbe.2019420

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Research article Special Issues

An effective classifier based on convolutional neural network and regularized extreme learning machine

College of Information Engineering, Xiangtan University, Xiangtan, Hunan 411105, China

Received: 24 April 2019 Accepted: 25 August 2019 Published: 18 September 2019

An effective classifier combining convolutional neural network and regularized extreme learning machine (called as CNN-RELM) is presented in this paper. Firstly, CNN-RELM trains the convolutional neural network (CNN) using the gradient descent method until the learning target accuracy reaches. Then the fully connected layer of CNN is replaced by regularized extreme learning machine (RELM) optimized by genetic algorithm and the rest layers of the CNN remain unchanged. The experiments on different face databases are given to evaluate the performance of CNN-RELM. The experimental results show that CNN-RELM is a feasible classifier and it outperforms CNN and RELM. Due to the uniting of CNN and RELM, CNN-RELM have the advantages of CNN and RELM and it is easier to learn and faster in testing.
- convolutional neural network,
- regularized extreme learning machine,
- classification,
- face recognition,
- feature extraction
Citation: Chunmei He, Hongyu Kang, Tong Yao, Xiaorui Li. An effective classifier based on convolutional neural network and regularized extreme learning machine[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 8309-8321. doi: 10.3934/mbe.2019420

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Abstract

An effective classifier combining convolutional neural network and regularized extreme learning machine (called as CNN-RELM) is presented in this paper. Firstly, CNN-RELM trains the convolutional neural network (CNN) using the gradient descent method until the learning target accuracy reaches. Then the fully connected layer of CNN is replaced by regularized extreme learning machine (RELM) optimized by genetic algorithm and the rest layers of the CNN remain unchanged. The experiments on different face databases are given to evaluate the performance of CNN-RELM. The experimental results show that CNN-RELM is a feasible classifier and it outperforms CNN and RELM. Due to the uniting of CNN and RELM, CNN-RELM have the advantages of CNN and RELM and it is easier to learn and faster in testing.

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