{"title":"自动离线多变量数据分析","authors":"G. Sebestyen","doi":"10.1145/1464291.1464365","DOIUrl":null,"url":null,"abstract":"Many research problems in the social and physical sciences require the collection of large amounts of data of the simultaneously measured attributes of a phenomenon or process under investigation. Pattern recognition problems, in particular, yield data of multiple variables for each manifestation of the different sources of data. The automatic off-line multivariate analysis techniques described in this paper deal with the quantitative description of data of this type.","PeriodicalId":297471,"journal":{"name":"AFIPS '66 (Fall)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1899-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Automatic off-line multivariate data analysis\",\"authors\":\"G. Sebestyen\",\"doi\":\"10.1145/1464291.1464365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many research problems in the social and physical sciences require the collection of large amounts of data of the simultaneously measured attributes of a phenomenon or process under investigation. Pattern recognition problems, in particular, yield data of multiple variables for each manifestation of the different sources of data. The automatic off-line multivariate analysis techniques described in this paper deal with the quantitative description of data of this type.\",\"PeriodicalId\":297471,\"journal\":{\"name\":\"AFIPS '66 (Fall)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1899-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AFIPS '66 (Fall)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1464291.1464365\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AFIPS '66 (Fall)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1464291.1464365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Many research problems in the social and physical sciences require the collection of large amounts of data of the simultaneously measured attributes of a phenomenon or process under investigation. Pattern recognition problems, in particular, yield data of multiple variables for each manifestation of the different sources of data. The automatic off-line multivariate analysis techniques described in this paper deal with the quantitative description of data of this type.