An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed method is based on the learned radial basis function (RBF) kernel as the research object. The kernel proposed here can be considered a subspace region consisting of the...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-16
Main Authors: Wen, Hui, Li, Tongbin, Chen, Deli, Yang, Jianlu, Che, Yan
Format: Article
Language:English
Subjects:
Classification
Classifiers
Clustering
Datasets
Engineering
Kernels
Learning
Methods
Neural networks
Normal distribution
Optimization
Optimization algorithms
Probability distribution
Radial basis function
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Summary:An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed method is based on the learned radial basis function (RBF) kernel as the research object. The kernel proposed here can be considered a subspace region consisting of the same pattern category in the training sample space. By extending the region of the sample space of the original instances, relevant information between instances can be obtained from the subspace, and the classifier’s boundary can be far from the original instances; thus, the robustness and generalization performance of the classifier are enhanced. In concrete implementation, a new pattern vector is generated within each RBF kernel according to the instance optimization and screening method to characterize KHLD. Experiments on artificial datasets and several UCI benchmark datasets show the effectiveness of our method.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/8857818