计算机科学 ›› 2015, Vol. 42 ›› Issue (6): 82-87.doi: 10.11896/j.issn.1002-137X.2015.06.019
徐久成,刘洋洋,杜丽娜,孙林
XU Jiu-cheng, LIU Yang-yang, DU Li-na and SUN Lin
摘要: 针对典型的支持向量机增量学习算法对有用信息的丢失和现有支持向量机增量学习算法单纯追求分类器精准性的客观性,将三支决策损失函数的主观性引入支持向量机增量学习算法中,提出了一种基于三支决策的支持向量机增量学习方法。首先采用特征距离与中心距离的比值来计算三支决策中的条件概率;然后把三支决策中的边界域作为边界向量加入到原支持向量和新增样本中一起训练;最后,通过仿真实验证明,该方法不仅充分利用有用信息提高了分类准确性,而且在一定程度上修正了现有支持向量机增量学习算法的客观性,并解决了三支决策中条件概率的计算问题。
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