{"title":"基于机器学习的制造系统分类","authors":"T. Witkowski, P. Antczak, A. Antczak","doi":"10.1109/IDAACS.2011.6072833","DOIUrl":null,"url":null,"abstract":"This paper shows the application of inductive learning for acquisition of knowledge in manufacturing system. The software of this models, allows us to analyze the process construction rules for many variants reflecting a variety of combinations other factors. A sets of attributes selected for generating the necessary training examples for the machine learning algorithm have been used. Experiments indicate that these methods are well suited to do the classification of manufacturing problems.","PeriodicalId":106306,"journal":{"name":"Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Machine learning — Based classification in manufacturing system\",\"authors\":\"T. Witkowski, P. Antczak, A. Antczak\",\"doi\":\"10.1109/IDAACS.2011.6072833\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper shows the application of inductive learning for acquisition of knowledge in manufacturing system. The software of this models, allows us to analyze the process construction rules for many variants reflecting a variety of combinations other factors. A sets of attributes selected for generating the necessary training examples for the machine learning algorithm have been used. Experiments indicate that these methods are well suited to do the classification of manufacturing problems.\",\"PeriodicalId\":106306,\"journal\":{\"name\":\"Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems\",\"volume\":\"95 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IDAACS.2011.6072833\",\"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 of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IDAACS.2011.6072833","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning — Based classification in manufacturing system
This paper shows the application of inductive learning for acquisition of knowledge in manufacturing system. The software of this models, allows us to analyze the process construction rules for many variants reflecting a variety of combinations other factors. A sets of attributes selected for generating the necessary training examples for the machine learning algorithm have been used. Experiments indicate that these methods are well suited to do the classification of manufacturing problems.