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Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection

Nuclear methods, such as the study of the main components of nuclear and the support of vector machines, have gradually evolved into a type of pillar methods for pattern recognition and economic statistics. Therefore, how to choose the inner product function of high-dimensional space is particularly...

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Autores principales: Yao, Yan, Huang, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512609/
https://www.ncbi.nlm.nih.gov/pubmed/36172320
http://dx.doi.org/10.1155/2022/1064657
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author Yao, Yan
Huang, Yan
author_facet Yao, Yan
Huang, Yan
author_sort Yao, Yan
collection PubMed
description Nuclear methods, such as the study of the main components of nuclear and the support of vector machines, have gradually evolved into a type of pillar methods for pattern recognition and economic statistics. Therefore, how to choose the inner product function of high-dimensional space is particularly important for the use of such methods. And, because the inner product function of high-dimensional space often determines the structure of high-dimensional space, the superiority of the kernel method is directly affected by using high-dimensional space. Due to its superior performance, the inner product function of Gaussian high-dimensional space has been put into practical use to a large extent. For the processing of unbalanced distribution of BGP data, because the results of BGP usually have multiple results, and the distribution is not uniform, so in the process of training SVM, the process of determining the composition of the hyperplane is generated due to various results The effect of the results will also be different so that the final planning equation has errors and weakens the classification ability. Finally, the BGP test results are displayed. The results show that the operation of the SVM algorithm can complete the supervision and management of BGP abnormal data. If there is any data fluctuation, an alarm will be issued. Under the condition of ensuring the correct rate, the input characteristics of BGP can be reduced in dimensionality, and specific selection methods can be used to complete. A high-level algorithm is used here, so that it has a better SVM classification effect, and abnormal traffic can be more conveniently and quickly checked out of BGP. It can be seen from the results of statistical analysis of urban and rural data that the current gap between urban and rural economic development level is too large, and to some extent, there is a trend of increasing fault, which will undoubtedly affect the stability of urban and rural areas, so the urban-rural income gap should be narrowed. We have also studied the principle of the general preferential economy in rural areas and put forward relevant effective strategy.
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spelling pubmed-95126092022-09-27 Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection Yao, Yan Huang, Yan Comput Intell Neurosci Research Article Nuclear methods, such as the study of the main components of nuclear and the support of vector machines, have gradually evolved into a type of pillar methods for pattern recognition and economic statistics. Therefore, how to choose the inner product function of high-dimensional space is particularly important for the use of such methods. And, because the inner product function of high-dimensional space often determines the structure of high-dimensional space, the superiority of the kernel method is directly affected by using high-dimensional space. Due to its superior performance, the inner product function of Gaussian high-dimensional space has been put into practical use to a large extent. For the processing of unbalanced distribution of BGP data, because the results of BGP usually have multiple results, and the distribution is not uniform, so in the process of training SVM, the process of determining the composition of the hyperplane is generated due to various results The effect of the results will also be different so that the final planning equation has errors and weakens the classification ability. Finally, the BGP test results are displayed. The results show that the operation of the SVM algorithm can complete the supervision and management of BGP abnormal data. If there is any data fluctuation, an alarm will be issued. Under the condition of ensuring the correct rate, the input characteristics of BGP can be reduced in dimensionality, and specific selection methods can be used to complete. A high-level algorithm is used here, so that it has a better SVM classification effect, and abnormal traffic can be more conveniently and quickly checked out of BGP. It can be seen from the results of statistical analysis of urban and rural data that the current gap between urban and rural economic development level is too large, and to some extent, there is a trend of increasing fault, which will undoubtedly affect the stability of urban and rural areas, so the urban-rural income gap should be narrowed. We have also studied the principle of the general preferential economy in rural areas and put forward relevant effective strategy. Hindawi 2022-09-19 /pmc/articles/PMC9512609/ /pubmed/36172320 http://dx.doi.org/10.1155/2022/1064657 Text en Copyright © 2022 Yan Yao and Yan Huang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yao, Yan
Huang, Yan
Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection
title Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection
title_full Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection
title_fullStr Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection
title_full_unstemmed Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection
title_short Impact of Financial Development on Income Gap Based on Improved Gaussian Kernel Function and BGP Anomaly Detection
title_sort impact of financial development on income gap based on improved gaussian kernel function and bgp anomaly detection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512609/
https://www.ncbi.nlm.nih.gov/pubmed/36172320
http://dx.doi.org/10.1155/2022/1064657
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