基于支持向量机的人才分类方法

Jing-jun Ye, Hua Hu, Chunlai Chai
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引用次数: 8

摘要

如今,任何一个招聘网站每天都会收到大量的个人信息和招聘信息。但大多数信息不能得到很好的分析,不能满足招聘要求。事实上,招聘单位寻找的是高层次人才和低层次人才。然而,许多人才信息不能被正确评估,从而导致求职者失去工作机会。本文将研究一种非线性二次分类方法在工作现场人事数据分类中的应用。该方法是基于径向基函数支持的支持向量机。用这种分类方法对样本数据进行分类,得到了比决策树等分类方法更令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Talent Classification Method Based on SVM
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.
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