摘要
在某市住房公积金管理中心的人力资源配置过程中,通过实例研究发现,采用支持向量机的人岗匹配度测算模型相对于BP人工神经网络等其他常规模型匹配精度更高,在样本数量较少的情况下其优越性更加突出。组织在采用支持向量机模型和模糊综合评价法进行人岗匹配度评价时,先应通过科学的工作分析,建立合适的测算指标体系,然后构建岗位匹配度矩阵和岗位候选人模糊矩阵,最后运用最小二乘支持向量机测算匹配度。
In the process of human resource deployment for one city housing fund management center,it can be found that measuring models of person-post matching based on support vector machine have greater precision than the models based on BP artificial neural networks,and also have a more favorable effect in the case of less sample quantity through the instance study.If organizations plan to evaluate the talent-post matching based on the measuring models of support vector machine and fuzzy comprehensive evaluation method,they should first establish proper measurement index system by the scientific job analysis,then structure post matching degree matrix and post candidate fuzzy matrices,and finally measure and calculate matching degree with the method of least square support vector machines.
出处
《中南林业科技大学学报（社会科学版）》
2011年第6期92-94,共3页
Journal of Central South University of Forestry & Technology（Social Sciences）
关键词
人岗匹配
支持向量机
模糊综合评价法
talent-post matching
support vector machine
fuzzy comprehensive evaluation method
