{"title":"通过交换ML模型进行集体自我学习","authors":"M. Ruiz, F. Boitier, P. Layec, Luis Velasco","doi":"10.1049/cp.2019.0985","DOIUrl":null,"url":null,"abstract":"Collective self-learning based on Machine Learning (ML) model sharing and combination is proposed to accelerate ML-based algorithm deployment. The considered architecture is presented, together with different alternatives for combining ML models. Performance analysis is carried out on an illustrative use case for autonomic optical transmission.","PeriodicalId":6826,"journal":{"name":"45th European Conference on Optical Communication (ECOC 2019)","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Collective self-learning by exchanging ML models\",\"authors\":\"M. Ruiz, F. Boitier, P. Layec, Luis Velasco\",\"doi\":\"10.1049/cp.2019.0985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Collective self-learning based on Machine Learning (ML) model sharing and combination is proposed to accelerate ML-based algorithm deployment. The considered architecture is presented, together with different alternatives for combining ML models. Performance analysis is carried out on an illustrative use case for autonomic optical transmission.\",\"PeriodicalId\":6826,\"journal\":{\"name\":\"45th European Conference on Optical Communication (ECOC 2019)\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"45th European Conference on Optical Communication (ECOC 2019)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1049/cp.2019.0985\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"45th European Conference on Optical Communication (ECOC 2019)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/cp.2019.0985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Collective self-learning based on Machine Learning (ML) model sharing and combination is proposed to accelerate ML-based algorithm deployment. The considered architecture is presented, together with different alternatives for combining ML models. Performance analysis is carried out on an illustrative use case for autonomic optical transmission.
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