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中文摘要：
为了提高孪生支持向量机的泛化能力,提出一种新的孪生大间隔分布机算法,以增加间隔分布对于训练模型的影响.理论研究表明,间隔分布对于模型的泛化性能有着非常重要的影响.该算法在标准孪生支持向量机优化目标函数上增加了间隔分布的影响,间隔分布通过一阶和二阶数据统计特征来体现.在标准数据集上的实验结果表明,所提出的算法比SVM、TWSVM、TBSVM算法的分类精确度更高.
英文摘要：
In order to improve the generalization ability of the twin support vector machine（TWSVM）, a novel twin large margin distribution machine（TLDM） which increases the impact of the margin distribution on the training model is proposed.Theoretical studies show that margin distribution has important influence on the generalization performance of the model.The proposed approach based on the standard twin support vector machine adds the affection of margin distribution to the optimization objective function. The margin distribution is characterized by first order and second order statistics. The experimental results based on benchmark data sets show that the proposed approach has better classification accuracy than other three algorithms including SVM, TWSVM and TBSVM.
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