Research on Generalized Hybrid Probability Convolutional Neural Network
This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probability convolutional neural network (GHP-CNN) is pr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/11301 |
| _version_ | 1827647264420528128 |
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| author | Wenyi Zhou Hongguang Fan Jihong Zhu Hui Wen Ying Xie |
| author_facet | Wenyi Zhou Hongguang Fan Jihong Zhu Hui Wen Ying Xie |
| author_sort | Wenyi Zhou |
| collection | DOAJ |
| description | This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probability convolutional neural network (GHP-CNN) is proposed to solve abstract feature classification with an unknown distribution form. To measure the generalization ability of the CFM, a new index is defined and the positive correlation between it and the CFM is researched. Generally, a fully trained CFM can extract features that are beneficial to classification, regardless of whether the data participate in training the CFM. In the CNN, the fully connected layer in the MLP is not always optimal, and the extracted abstract feature has an unknown distribution. Thus, an improved classifier called the structure-optimized probabilistic neural network (SOPNN) is used for abstract feature classification in the GHP-CNN. In the SOPNN, the separability information is not lost in the normalization process, and the final classification surface is close to the optimal classification surface under the Bayesian criterion. The proposed GHP-CNN utilizes the generalization ability of the CFM and the classification ability of the SOPNN. Experiments show that the proposed network has better classification ability than the existing hybrid neural networks. |
| first_indexed | 2024-03-09T19:16:44Z |
| format | Article |
| id | doaj.art-046e8c7e03814dad96d13968b6c38479 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:16:44Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-046e8c7e03814dad96d13968b6c384792023-11-24T03:41:22ZengMDPI AGApplied Sciences2076-34172022-11-0112211130110.3390/app122111301Research on Generalized Hybrid Probability Convolutional Neural NetworkWenyi Zhou0Hongguang Fan1Jihong Zhu2Hui Wen3Ying Xie4College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, ChinaCollege of Computer, Chengdu University, Chengdu 610106, ChinaCollege of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, ChinaNew Engineering Industry College, Putian University, Putian 351100, ChinaNew Engineering Industry College, Putian University, Putian 351100, ChinaThis paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probability convolutional neural network (GHP-CNN) is proposed to solve abstract feature classification with an unknown distribution form. To measure the generalization ability of the CFM, a new index is defined and the positive correlation between it and the CFM is researched. Generally, a fully trained CFM can extract features that are beneficial to classification, regardless of whether the data participate in training the CFM. In the CNN, the fully connected layer in the MLP is not always optimal, and the extracted abstract feature has an unknown distribution. Thus, an improved classifier called the structure-optimized probabilistic neural network (SOPNN) is used for abstract feature classification in the GHP-CNN. In the SOPNN, the separability information is not lost in the normalization process, and the final classification surface is close to the optimal classification surface under the Bayesian criterion. The proposed GHP-CNN utilizes the generalization ability of the CFM and the classification ability of the SOPNN. Experiments show that the proposed network has better classification ability than the existing hybrid neural networks.https://www.mdpi.com/2076-3417/12/21/11301machine learningBayesian classifierconvolutional neural network |
| spellingShingle | Wenyi Zhou Hongguang Fan Jihong Zhu Hui Wen Ying Xie Research on Generalized Hybrid Probability Convolutional Neural Network Applied Sciences machine learning Bayesian classifier convolutional neural network |
| title | Research on Generalized Hybrid Probability Convolutional Neural Network |
| title_full | Research on Generalized Hybrid Probability Convolutional Neural Network |
| title_fullStr | Research on Generalized Hybrid Probability Convolutional Neural Network |
| title_full_unstemmed | Research on Generalized Hybrid Probability Convolutional Neural Network |
| title_short | Research on Generalized Hybrid Probability Convolutional Neural Network |
| title_sort | research on generalized hybrid probability convolutional neural network |
| topic | machine learning Bayesian classifier convolutional neural network |
| url | https://www.mdpi.com/2076-3417/12/21/11301 |
| work_keys_str_mv | AT wenyizhou researchongeneralizedhybridprobabilityconvolutionalneuralnetwork AT hongguangfan researchongeneralizedhybridprobabilityconvolutionalneuralnetwork AT jihongzhu researchongeneralizedhybridprobabilityconvolutionalneuralnetwork AT huiwen researchongeneralizedhybridprobabilityconvolutionalneuralnetwork AT yingxie researchongeneralizedhybridprobabilityconvolutionalneuralnetwork |