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边界偏转覆盖增量支持向量机

DOI: 10.13190/jbupt.201004.30.zhangl, PP. 30-34

Keywords: 故障诊断,支持向量机,增量学习,模型更新

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Abstract:

为了利用不断积累的网络样本提高故障诊断效能,针对标准支持向量机不直接支持增量学习的问题,提出一种边界偏转覆盖增量支持向量机.根据违背KarushKuhnTucker条件的新增样本在特征空间中可引起原分类边界改变的情况,设计边界偏转覆盖算法预选支持向量再生区作为增量训练工作集,解决了难以确定的非支持向量向支持向量的转化问题.理论分析和实验结果表明,该方法能有效简化训练工作集,在保证故障诊断精度的同时大幅度提高增量训练效率.

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