{"title":"一类Mercer核最小二乘回归的学习率","authors":"Bao-huai Sheng, Liqin Duan, Peixin Ye","doi":"10.1109/ISDEA.2012.633","DOIUrl":null,"url":null,"abstract":"We consider the error estimate of least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. When the kernel belongs to some kind of Mercer kernel, under a mild regularity condition on the regression function, we derive a dimensional-free learning rate m-1/6.","PeriodicalId":267532,"journal":{"name":"2012 Second International Conference on Intelligent System Design and Engineering Application","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning Rate of Least Square Regressions with Some Kind of Mercer Kernel\",\"authors\":\"Bao-huai Sheng, Liqin Duan, Peixin Ye\",\"doi\":\"10.1109/ISDEA.2012.633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the error estimate of least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. When the kernel belongs to some kind of Mercer kernel, under a mild regularity condition on the regression function, we derive a dimensional-free learning rate m-1/6.\",\"PeriodicalId\":267532,\"journal\":{\"name\":\"2012 Second International Conference on Intelligent System Design and Engineering Application\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Second International Conference on Intelligent System Design and Engineering Application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDEA.2012.633\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Second International Conference on Intelligent System Design and Engineering Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDEA.2012.633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning Rate of Least Square Regressions with Some Kind of Mercer Kernel
We consider the error estimate of least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. When the kernel belongs to some kind of Mercer kernel, under a mild regularity condition on the regression function, we derive a dimensional-free learning rate m-1/6.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
