计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 260-264.doi: 10.11896/j.issn.1002-137X.2017.08.044
李凯,顾丽凤,胡少方
LI Kai, GU Li-feng and HU Shao-fang
摘要： 模糊孪生支持向量机是一种重要的机器学习方法,克服了噪声或异常数据对分类的影响；然而,该方法考虑的仍是经验风险,从而使得训练过程易出现过拟合现象。为了解决该问题,通过引入调整项,提出了一种改进的模糊孪生支持向量机模型,利用二次规划求解方法和超松弛迭代法对模型进行求解,获得了用于分类的决策面。实验中选取UCI标准数据集验证了所提方法的有效性。
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