Qianru Huang, Qingmei Dong, Yunlong Liu, Deqing Ji, Qinwei Fan
{"title":"A Sigma-Pi-Sigma Neural Network Model with Graph Regularity Term","authors":"Qianru Huang, Qingmei Dong, Yunlong Liu, Deqing Ji, Qinwei Fan","doi":"10.54097/xwvpkd67","DOIUrl":null,"url":null,"abstract":"In recent years, Sigma-Pi-Sigma neural network (SPSNN) as a special kind of higher-order neural network has attracted wide attention for its fast convergence speed and good approximation ability. However, an inappropriate number of hidden layer neurons may also lead to model underfitting or overfitting, which affects the performance and generalization ability of the model. Therefore, we propose a Sigma-Pi-Sigma neural network with graph regularity by adding a graph regularity term to the network. The results show that the proposed algorithm performs well in terms of training accuracy, testing accuracy and efficiency.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Computing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54097/xwvpkd67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, Sigma-Pi-Sigma neural network (SPSNN) as a special kind of higher-order neural network has attracted wide attention for its fast convergence speed and good approximation ability. However, an inappropriate number of hidden layer neurons may also lead to model underfitting or overfitting, which affects the performance and generalization ability of the model. Therefore, we propose a Sigma-Pi-Sigma neural network with graph regularity by adding a graph regularity term to the network. The results show that the proposed algorithm performs well in terms of training accuracy, testing accuracy and efficiency.