{"title":"基于支持向量机的人才分类方法","authors":"Jing-jun Ye, Hua Hu, Chunlai Chai","doi":"10.1109/IUCE.2009.63","DOIUrl":null,"url":null,"abstract":"Nowadays, any employment and recruitment web sites receive immense personal information and recruit information every day. But most information can’t be properly analyzed and can’t meet the recruit requirement. In fact, the recruiting units are looking for talents of both high and low levels talents. However, many talents information can’t be evaluated correctly so that the appliers lose their job opportunities. This paper will research that a non-linear quadratic classification method applies in the personnel data from a job site. The method is support vector machine based on radial basis function support. According to this classification method classifying the sample data, we have got more satisfactory results than by another classification method such as decision tree.","PeriodicalId":153560,"journal":{"name":"2009 International Symposium on Intelligent Ubiquitous Computing and Education","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A Talent Classification Method Based on SVM\",\"authors\":\"Jing-jun Ye, Hua Hu, Chunlai Chai\",\"doi\":\"10.1109/IUCE.2009.63\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, any employment and recruitment web sites receive immense personal information and recruit information every day. But most information can’t be properly analyzed and can’t meet the recruit requirement. In fact, the recruiting units are looking for talents of both high and low levels talents. However, many talents information can’t be evaluated correctly so that the appliers lose their job opportunities. This paper will research that a non-linear quadratic classification method applies in the personnel data from a job site. The method is support vector machine based on radial basis function support. According to this classification method classifying the sample data, we have got more satisfactory results than by another classification method such as decision tree.\",\"PeriodicalId\":153560,\"journal\":{\"name\":\"2009 International Symposium on Intelligent Ubiquitous Computing and Education\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Symposium on Intelligent Ubiquitous Computing and Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IUCE.2009.63\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Symposium on Intelligent Ubiquitous Computing and Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IUCE.2009.63","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nowadays, any employment and recruitment web sites receive immense personal information and recruit information every day. But most information can’t be properly analyzed and can’t meet the recruit requirement. In fact, the recruiting units are looking for talents of both high and low levels talents. However, many talents information can’t be evaluated correctly so that the appliers lose their job opportunities. This paper will research that a non-linear quadratic classification method applies in the personnel data from a job site. The method is support vector machine based on radial basis function support. According to this classification method classifying the sample data, we have got more satisfactory results than by another classification method such as decision tree.