{"title":"容噪归纳学习的迭代规则简化","authors":"P. Pachowicz, J. Bala, Jianping Zhang","doi":"10.1109/TAI.1992.246447","DOIUrl":null,"url":null,"abstract":"An iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The first phase improves the quality of training data through a concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training data set.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Iterative rule simplification for noise tolerant inductive learning\",\"authors\":\"P. Pachowicz, J. Bala, Jianping Zhang\",\"doi\":\"10.1109/TAI.1992.246447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The first phase improves the quality of training data through a concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training data set.<<ETX>>\",\"PeriodicalId\":265283,\"journal\":{\"name\":\"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1992.246447\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1992.246447","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Iterative rule simplification for noise tolerant inductive learning
An iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The first phase improves the quality of training data through a concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training data set.<>
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