中文摘要：

支持向量机作为一种机器学习算法因其良好的推广性和强大的非线性处理能力而令人瞩目.为此将支持向量机与国家高考的实际数据相结合,以具体高校的高考模拟考试成绩为主要训练数据,进行学生的高考成绩预测.实验考虑了三种情形.一是通过六次模拟考试的特征分来预测高考的特征分;二是通过六次模拟考试和高考的特征分来预测高考的录取批次;三是通过六次模拟考试的特征分和高考的预测特征分来预测高考的录取批次.通过与神经网络算法的比较,实验结果均表明了支持向量机方法的稳定性和良好的预测性.

英文摘要：

Support vector machine（SVM）, one of machine learning methods, is very impressive for its good generalization and powerful nonlinearly processing ability. SVM was combined with national matriculation, where scores of six mock exams are taken as training data to predict the final admission scores. Three situations were considered. First, the scores of NMT were predicted using scores in six simulation tests. Second, the admission batch was predicted by using scores in six simulation tests and NMT. Third, the admission batch was predicted by using scores in six simulation tests and the estimated scores in NMT. In all experiments, SVMs were compared with neural networks （NNs）. Experimental results show that SVMs are much more stable and have better prediction ability.