Title :
A Talent Classification Method Based on SVM
Author :
Hu, Hua ; Ye, Jing ; Chai, Chunlai
Author_Institution :
Zhejiang Gongshang Univ., Hangzhou, China
Abstract :
Nowadays, any employment and recruitment Web sites receive immense personal information and recruit information every day. But most information canpsilat be properly analyzed and canpsilat meet the recruit requirement. In fact, the recruiting units are looking for talents of both high and low levels talents. However, many talents information canpsilat 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.
Keywords :
Web sites; classification; job specification; radial basis function networks; recruitment; support vector machines; SVM; job site; nonlinear quadratic classification method; personal information; radial basis function; recruitment Web site; support vector machine; talent classification method; Bayesian methods; Classification tree analysis; Computer science; Decision trees; Employment; Humans; Personnel; Recruitment; Support vector machine classification; Support vector machines; radial basis function; support vector machine; talent classification;
Conference_Titel :
Intelligent Ubiquitous Computing and Education, 2009 International Symposium on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3619-4
DOI :
10.1109/IUCE.2009.63