In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S2TSVM) is proposed by adding the original unlabe...In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S2TSVM) is proposed by adding the original unlabeled samples. In S2TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples, and a pre-classified S2TSVM (PS2TSVM) is proposed. Compared with S2TSVM, PS2TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS2TSVM is smoothed. After smoothing the model, the pre-classified smooth S2TSVM (PS3TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS3TSVM model has better classification results.展开更多
对支持向量机(Twin Support Vector Machine,TWSVM)的优化思想源于基于广义特征值近似支持向量机(ProximalSVM based on Generalized Eigenvalues,GEPSVM)。该算法将传统SVM问题分解为两个凸规划问题,使得训练速度缩减到原来的1/4。对TW...对支持向量机(Twin Support Vector Machine,TWSVM)的优化思想源于基于广义特征值近似支持向量机(ProximalSVM based on Generalized Eigenvalues,GEPSVM)。该算法将传统SVM问题分解为两个凸规划问题,使得训练速度缩减到原来的1/4。对TWSVM做了修正,基于新的优化准则设计了一种特殊TWSVM(GTWSVM),在此基础上,提出了快速GTWSVM(FGTWSVM),其将GTWSVM转换为无约束凸规划问题求解。该算法在保证得到与TWSVM相当的分类性能以及较快的计算速度的同时,还减少了输入空间的特征数以及内存占用。对于非线性问题,FGTWSVM可以减少核函数数目。展开更多
Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsi...Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.展开更多
为进一步提高有载分接开关(on-load tap changer,OLTC)机械状态监测的准确性,文中基于优化品质因数可调小波变换(tunable quality wavelet transform,TQWT)对OLTC切换过程中的振动信号进行了分析。即使用人工鱼群算法(artificial fish s...为进一步提高有载分接开关(on-load tap changer,OLTC)机械状态监测的准确性,文中基于优化品质因数可调小波变换(tunable quality wavelet transform,TQWT)对OLTC切换过程中的振动信号进行了分析。即使用人工鱼群算法(artificial fish swarm algorithm,AFSA)基于分解余量与整体正交系数研究了TQWT的优化分解方法,计算得到了OLTC振动信号的多个子序列,构建了基于优化孪生支持向量机(twin support vector machine,TWSVM)的OLTC机械故障诊断模型。对某CM型OLTC正常与典型机械故障下振动信号的分析结果表明,所提优化TQWT分解方法有效提高了OLTC振动信号分解结果的准确性。相对于其他诊断模型,所构建AFSA-TWSVM的OLTC机械故障诊断模型分类效果好且收敛速度更快。展开更多
基金supported by the Fundamental Research Funds for University of Science and Technology Beijing(FRF-BR-12-021)
文摘In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S2TSVM) is proposed by adding the original unlabeled samples. In S2TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples, and a pre-classified S2TSVM (PS2TSVM) is proposed. Compared with S2TSVM, PS2TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS2TSVM is smoothed. After smoothing the model, the pre-classified smooth S2TSVM (PS3TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS3TSVM model has better classification results.
文摘对支持向量机(Twin Support Vector Machine,TWSVM)的优化思想源于基于广义特征值近似支持向量机(ProximalSVM based on Generalized Eigenvalues,GEPSVM)。该算法将传统SVM问题分解为两个凸规划问题,使得训练速度缩减到原来的1/4。对TWSVM做了修正,基于新的优化准则设计了一种特殊TWSVM(GTWSVM),在此基础上,提出了快速GTWSVM(FGTWSVM),其将GTWSVM转换为无约束凸规划问题求解。该算法在保证得到与TWSVM相当的分类性能以及较快的计算速度的同时,还减少了输入空间的特征数以及内存占用。对于非线性问题,FGTWSVM可以减少核函数数目。
基金supported by the National Natural Science Foundation of China(6177202062202433+4 种基金621723716227242262036010)the Natural Science Foundation of Henan Province(22100002)the Postdoctoral Research Grant in Henan Province(202103111)。
文摘Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.
文摘为进一步提高有载分接开关(on-load tap changer,OLTC)机械状态监测的准确性,文中基于优化品质因数可调小波变换(tunable quality wavelet transform,TQWT)对OLTC切换过程中的振动信号进行了分析。即使用人工鱼群算法(artificial fish swarm algorithm,AFSA)基于分解余量与整体正交系数研究了TQWT的优化分解方法,计算得到了OLTC振动信号的多个子序列,构建了基于优化孪生支持向量机(twin support vector machine,TWSVM)的OLTC机械故障诊断模型。对某CM型OLTC正常与典型机械故障下振动信号的分析结果表明,所提优化TQWT分解方法有效提高了OLTC振动信号分解结果的准确性。相对于其他诊断模型,所构建AFSA-TWSVM的OLTC机械故障诊断模型分类效果好且收敛速度更快。
