{"title":"随机森林核在球变量非参数回归模型中的应用","authors":"Huiqun Gao, Xu Qin","doi":"10.1109/ICAA53760.2021.00179","DOIUrl":null,"url":null,"abstract":"In this paper, we apply the random forest kernel to nonparametric regression model with spherical and linear variables. Validate the new model with simulated data and actual airfoil noise data. Comparing with the Gaussian kernel and the linear-circular kernel, the experimental results show that the random forest kernel has stable performance and fast computation speed, and the random forest kernel has a better fitting effect in high dimension.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of the Random Forest Kernel in Nonparametric Regression Model with Spherical Variables\",\"authors\":\"Huiqun Gao, Xu Qin\",\"doi\":\"10.1109/ICAA53760.2021.00179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we apply the random forest kernel to nonparametric regression model with spherical and linear variables. Validate the new model with simulated data and actual airfoil noise data. Comparing with the Gaussian kernel and the linear-circular kernel, the experimental results show that the random forest kernel has stable performance and fast computation speed, and the random forest kernel has a better fitting effect in high dimension.\",\"PeriodicalId\":121879,\"journal\":{\"name\":\"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAA53760.2021.00179\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAA53760.2021.00179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of the Random Forest Kernel in Nonparametric Regression Model with Spherical Variables
In this paper, we apply the random forest kernel to nonparametric regression model with spherical and linear variables. Validate the new model with simulated data and actual airfoil noise data. Comparing with the Gaussian kernel and the linear-circular kernel, the experimental results show that the random forest kernel has stable performance and fast computation speed, and the random forest kernel has a better fitting effect in high dimension.