{"title":"在线问题的一般算法框架。","authors":"Yair Censor, Simeon Reich, Alexander J Zaslavski","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>We study general algorithmic frameworks for online learning tasks. These include binary classification, regression, multiclass problems and cost-sensitive multiclass classification. The theorems that we present give loss bounds on the behavior of our algorithms which depend on general conditions on the iterative step sizes.</p>","PeriodicalId":88906,"journal":{"name":"International journal of pure and applied mathematics : IJPAM","volume":"46 1","pages":"19-36"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2875694/pdf/nihms-152176.pdf","citationCount":"0","resultStr":"{\"title\":\"General Algorithmic Frameworks for Online Problem.\",\"authors\":\"Yair Censor, Simeon Reich, Alexander J Zaslavski\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We study general algorithmic frameworks for online learning tasks. These include binary classification, regression, multiclass problems and cost-sensitive multiclass classification. The theorems that we present give loss bounds on the behavior of our algorithms which depend on general conditions on the iterative step sizes.</p>\",\"PeriodicalId\":88906,\"journal\":{\"name\":\"International journal of pure and applied mathematics : IJPAM\",\"volume\":\"46 1\",\"pages\":\"19-36\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2875694/pdf/nihms-152176.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of pure and applied mathematics : IJPAM\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of pure and applied mathematics : IJPAM","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
General Algorithmic Frameworks for Online Problem.
We study general algorithmic frameworks for online learning tasks. These include binary classification, regression, multiclass problems and cost-sensitive multiclass classification. The theorems that we present give loss bounds on the behavior of our algorithms which depend on general conditions on the iterative step sizes.
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