{"title":"聚类分析分类——半监督分类中的集成技术","authors":"A. Jurek, Y. Bi, Shengli Wu, C. Nugent","doi":"10.1109/ICTAI.2011.137","DOIUrl":null,"url":null,"abstract":"In this work we adopt a previously introduced meta-learning classification method for semi-supervised learning problems. In our previous work we illustrated that the method is successful when applied in a supervised classification problem. In our current work the results demonstrate that following refinements made to the method it can be successfully applied to semi-supervised classification cases.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"494 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Classification by Clusters Analysis - An Ensemble Technique in a Semi-supervised Classification\",\"authors\":\"A. Jurek, Y. Bi, Shengli Wu, C. Nugent\",\"doi\":\"10.1109/ICTAI.2011.137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work we adopt a previously introduced meta-learning classification method for semi-supervised learning problems. In our previous work we illustrated that the method is successful when applied in a supervised classification problem. In our current work the results demonstrate that following refinements made to the method it can be successfully applied to semi-supervised classification cases.\",\"PeriodicalId\":332661,\"journal\":{\"name\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"volume\":\"494 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2011.137\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2011.137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification by Clusters Analysis - An Ensemble Technique in a Semi-supervised Classification
In this work we adopt a previously introduced meta-learning classification method for semi-supervised learning problems. In our previous work we illustrated that the method is successful when applied in a supervised classification problem. In our current work the results demonstrate that following refinements made to the method it can be successfully applied to semi-supervised classification cases.