Gaoya dianqi, 2019 为解决基于支持向量机（SVM）的变压器故障诊断中因样本不对称导致诊断准确率降低的问题,提出了一种改进的向上采样策略和SVM结合的方法。首先通过K-近邻算法提取少数类样本数据中的边界数据集并生成新的少数类随机样本,在此基础上向少数类样本中添加人工生成的随机新样本使得两类样本数量达到基本均衡。对比均衡样本和不对称样本下的SVM分类模型的性能,结果表明:该方法能够有效降低SVM分类平面的偏移程度,进一步提高了SVM变压器故障诊断的准确率。
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[Construction of risk prediction models of hypothermia after transurethral holmium laser enucleation of the prostate based on three machine learning algorithms]. [PDF]
Nan Fang Yi Ke Da Xue Xue BaoJiang J, Feng S, Sun Y, An Y.
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[A pace recognition method for exoskeleton wearers based on support vector machine-hidden Markov model]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2022 Hu D, Liu Z, Chen L, Wang Q.
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Sheng Wu Yi Xue Gong Cheng Xue Za ZhiHan G, Sun Z, Tang X.
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Zhendong Ceshi yu Zhenduan针对螺杆转子铣削加工过程中的颤振问题，提出了一种基于RelifF算法优化最小二乘支持向量机（RelifF‑least square support vector machine， 简称RF‑LSSVM）的颤振监测方法。首先，使用变分模态分解（variational modal decomposition， 简称VMD）和RelifF算法对螺杆转子铣削过程中的振动信号进行分解、特征提取与选择；其次，利用增强鲸鱼算法（enhanced whale optimization algorithm， 简称E ...
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Zhongguo Dang Dai Er Ke Za ZhiWu ST, Wang CL, Shi DR.
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Se PuWang SM, Kang JW.
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Zhongguo Dang Dai Er Ke Za ZhiZhai HL, Tang Y.
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Zhongguo Xiu Fu Chong Jian Wai Ke Za ZhiYe Y, Lu B, Li J, Zhang K.
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