{"title":"基于蒙特卡罗仿真的自动学习机性能分析算法设计","authors":"Smail Tigani, M. Ouzzif, A. Hasbi","doi":"10.1109/NGNS.2014.6990221","DOIUrl":null,"url":null,"abstract":"This paper proposes advanced key performance indicators dedicated to learning machines and auto-adaptive systems performance analysis. This work introduces an algorithm implementing designed key performance indicators for automatic learning capacity checking. The algorithm simulates the supervised environment to stimulate the tested auto-adaptive machine and then study its adaption capacity based on indicators statistically designed.","PeriodicalId":138330,"journal":{"name":"2014 International Conference on Next Generation Networks and Services (NGNS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monte Carlo simulation based algorithm design for automatic learning machine performance analysis\",\"authors\":\"Smail Tigani, M. Ouzzif, A. Hasbi\",\"doi\":\"10.1109/NGNS.2014.6990221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes advanced key performance indicators dedicated to learning machines and auto-adaptive systems performance analysis. This work introduces an algorithm implementing designed key performance indicators for automatic learning capacity checking. The algorithm simulates the supervised environment to stimulate the tested auto-adaptive machine and then study its adaption capacity based on indicators statistically designed.\",\"PeriodicalId\":138330,\"journal\":{\"name\":\"2014 International Conference on Next Generation Networks and Services (NGNS)\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Next Generation Networks and Services (NGNS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NGNS.2014.6990221\",\"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 International Conference on Next Generation Networks and Services (NGNS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NGNS.2014.6990221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monte Carlo simulation based algorithm design for automatic learning machine performance analysis
This paper proposes advanced key performance indicators dedicated to learning machines and auto-adaptive systems performance analysis. This work introduces an algorithm implementing designed key performance indicators for automatic learning capacity checking. The algorithm simulates the supervised environment to stimulate the tested auto-adaptive machine and then study its adaption capacity based on indicators statistically designed.