面向就业网站的高校毕业生分类机制研究——基于支持向量机的方法

目前，大多数就业网站及招聘公司每日都会新增巨大的人才信息数据，其中高校应届毕业生占绝大部分。如果没有进行科学的整理和分析，将会流失很多珍贵的人才资源，本文基于支持向量机算法，针对具体招聘岗位，来研究就业网站数据库中的高校毕业生分类问题。在具体岗位中，通过这种分类方法对毕业生样本的分类过滤，得到了比较理想的结果，预计能很好的将海量毕业生数据合理的分配岗位。由此，科学的整理和评价各类人才信息，可以大大的提高招聘单位对毕业生数据库的管理效率。预计此方法在高校毕业生就业领域具有较强的应用性。

The Employment-Network-Oriented College Graduates Classification Mechanism--Based on the Support Vector Machine Method

Nowadays, any employment and recruitment web sites receive immense personal information and recruit information every day, in which there is the vast majority of the college graduate information. If they can not be collected and analyzed scientifically, it will lost lots of talents resource. This paper study the college graduates classification in the employment web database based on support vector machine algorithm for the position-specific recruitment. According to this classification method classifying the sample data, we have got more satisfactory results. Therefore, the collation of various types of talent resume scientifically can greatly improve the efficiency of management of some recruit unit on the human resources. This method is good to be applied in the graduate’s employment.