{"title":"为了更好的预测，控制你的信息","authors":"Michal Moshkovitz, Naftali Tishby","doi":"10.1109/ISIT.2014.6874966","DOIUrl":null,"url":null,"abstract":"We suggest a unified view of two known prediction algorithms: Context Tree Weighting (CTW) and Prediction Suffix Tree (PST), by formulating them as information limited control problems. Using a unified view of planning and information gathering we suggest a new algorithm that combines the advantages of these two extreme algorithms and interpolates efficiently between them. The unified view is based on recent ideas from optimal control under information constraints.","PeriodicalId":127191,"journal":{"name":"2014 IEEE International Symposium on Information Theory","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Control your information for better predictions\",\"authors\":\"Michal Moshkovitz, Naftali Tishby\",\"doi\":\"10.1109/ISIT.2014.6874966\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We suggest a unified view of two known prediction algorithms: Context Tree Weighting (CTW) and Prediction Suffix Tree (PST), by formulating them as information limited control problems. Using a unified view of planning and information gathering we suggest a new algorithm that combines the advantages of these two extreme algorithms and interpolates efficiently between them. The unified view is based on recent ideas from optimal control under information constraints.\",\"PeriodicalId\":127191,\"journal\":{\"name\":\"2014 IEEE International Symposium on Information Theory\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Symposium on Information Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIT.2014.6874966\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Symposium on Information Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.2014.6874966","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We suggest a unified view of two known prediction algorithms: Context Tree Weighting (CTW) and Prediction Suffix Tree (PST), by formulating them as information limited control problems. Using a unified view of planning and information gathering we suggest a new algorithm that combines the advantages of these two extreme algorithms and interpolates efficiently between them. The unified view is based on recent ideas from optimal control under information constraints.
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