摘要
支持向量机是近几年发展起来的机器学习方法,它是利用接近边界的少数向量来构造一个最优分类面。然而当两类中的样本数量差别悬殊时,支持向量机的分类能力会下降。为了解决此问题,文中提出了一种改进的支持向量机算法———DFP SVM算法。实验表明,此方法在解决两类样本数量十分不均衡问题时有着很强的分类能力。
Support vector machine is an algorithm of machine learning that has developed during these years. It constructs an optimal hyperplane utilizing a small set of vectors near boundary. However, when the two-class problem samples are very unbalanced, SVM has a poor performance. An improved SVM: DFP-SVM was presented here. Computational results indicate that the modified algorithm has a strong capability of classification for the unbalanced samples of the two-class problems.
出处
《计算机应用》
CSCD
北大核心
2004年第12期14-15,共2页
journal of Computer Applications
基金
国家自然科学基金资助项目 (6 0 3 72 0 72 )