基于深层数据分类高校就业率计算优化模型

Based on the Deep Data Classification Optimization Model of University Employment Rate Calculation

在高校就业率统计过程中,需要利用支持向量机方法进行统计。由于这种统计方法计算复杂度比较高,运算时间比较长,造成高校就业率统计的效率较低。为此,本文提出了一种基于支持向量机优化算法的高校就业率统计方法。对高校就业样本进行分类处理,并将分类的结果进行统计。实验结果表明,这种算法能够有效提高高校就业率统计的效率,取得了令人满意的效果。

In university employment statistics process,need to use support vector machine method of statistics.Because of this statistical method computational complexity is higher,operation time is long,cause the employment statistics low efficiency.Therefore need to this kind of statistical method optimized.Therefore,this paper proposes a based on support vector machine（SVM） optimization algorithm of university employment statistics method.To the university employment sample classification processing,and the classification results were statistically.The experimental results show that this algorithm can effectively improve the efficiency of the university employment statistics,and achieved satisfactory effect.