A Talent Classification Method Based on SVM
Jing Ye, Hua Hu, Chunlai Chai · 2009
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.