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Fault diagnosis using a probability least squares support vector classification machine 被引量:4
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作者 GAO Yang, WANG Xuesong, CHENG Yuhu, PAN Jie School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China 《Mining Science and Technology》 EI CAS 2010年第6期917-921,共5页
Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines ... Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM. 展开更多
关键词 fault diagnosis PROBABILITY least squares support vector classification machine roller bearing
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Quintic spline smooth semi-supervised support vector classification machine 被引量:1
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作者 Xiaodan Zhang Jinggai Ma +1 位作者 Aihua Li Ang Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第3期626-632,共7页
A semi-supervised vector machine is a relatively new learning method using both labeled and unlabeled data in classifi- cation. Since the objective function of the model for an unstrained semi-supervised vector machin... A semi-supervised vector machine is a relatively new learning method using both labeled and unlabeled data in classifi- cation. Since the objective function of the model for an unstrained semi-supervised vector machine is not smooth, many fast opti- mization algorithms cannot be applied to solve the model. In order to overcome the difficulty of dealing with non-smooth objective functions, new methods that can solve the semi-supervised vector machine with desired classification accuracy are in great demand. A quintic spline function with three-times differentiability at the ori- gin is constructed by a general three-moment method, which can be used to approximate the symmetric hinge loss function. The approximate accuracy of the quintic spiine function is estimated. Moreover, a quintic spline smooth semi-support vector machine is obtained and the convergence accuracy of the smooth model to the non-smooth one is analyzed. Three experiments are performed to test the efficiency of the model. The experimental results show that the new model outperforms other smooth models, in terms of classification performance. Furthermore, the new model is not sensitive to the increasing number of the labeled samples, which means that the new model is more efficient. 展开更多
关键词 SEMI-SUPERVISED support vector classification machine SMOOTH quintic spline function convergence.
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Support vector classification for SAR of 5-HT3 receptor antagonists 被引量:1
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作者 杨善升 陆文聪 +1 位作者 纪晓波 陈念贻 《Journal of Shanghai University(English Edition)》 CAS 2006年第4期366-370,共5页
In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3 (5-HT3 ) receptor antagonists with 26 compounds. In a b... In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3 (5-HT3 ) receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network (BP ANN), K-nearest neighbor (KNN) and Fisher methods. 展开更多
关键词 support vector classification structure-activity relationship CHEMOMETRICS 5-HT3 receptor antagonists.
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Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification 被引量:11
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作者 Zhan-yu LIU Jing-jing SHI +1 位作者 Li-wen ZHANG Jing-feng HUANG 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE CAS CSCD 2010年第1期71-78,共8页
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflec... Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles. 展开更多
关键词 Rice panicle Principal component analysis (PCA) support vector classification (SVC) Hyperspectra reflectance Derivative spectra
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ERROR ANALYSIS OF MULTICATEGORY SUPPORT VECTOR MACHINE CLASSIFIERS
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作者 Lei Ding BaohuaiSheng 《Analysis in Theory and Applications》 2010年第2期153-173,共21页
The paper is related to the error analysis of Multicategory Support Vector Machine (MSVM) classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernel as Mercer kernel and give the error e... The paper is related to the error analysis of Multicategory Support Vector Machine (MSVM) classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernel as Mercer kernel and give the error estimate with De La Vall6e Poussin means. We also introduce the standard estimation of sample error, and derive the explicit learning rate. 展开更多
关键词 support vector machine classification learning rate reproducing kernel Hilbert spaces De La Vall^e Poussin means
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Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment
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作者 SOOMRO Bushra Naz XIAO Liang +1 位作者 SOOMRO Shahzad Hyder MOLAEI Mohsen 《Journal of Donghua University(English Edition)》 EI CAS 2016年第6期954-960,共7页
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image (HSI) classification was presented. For this purpose, standard support vector machines ( SVMs ), mudttnomial l... A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image (HSI) classification was presented. For this purpose, standard support vector machines ( SVMs ), mudttnomial logistic regression ( MLR ) and sparse representation (SR) based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased. 展开更多
关键词 learning algorithms hyper-spectral image classification support vector machine(SVM) multinomial logistic regression(MLR) elastic net regression(ELNR) sparse representation(SR) spatial-aware
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GA-SVC model and application of comprehensive evaluation of coal mine essential safety management
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作者 Zhi-Jun WANG Rui-Lin ZHANG Wen-Ting SONG 《Journal of Coal Science & Engineering(China)》 2013年第2期226-230,共5页
In order to evaluate the level of the coal mine essential safety management, the comprehensive index system was designed base on the connotation principle of the mine essential safety management. Due to the disadvanta... In order to evaluate the level of the coal mine essential safety management, the comprehensive index system was designed base on the connotation principle of the mine essential safety management. Due to the disadvantage of index weight setting by subjective idea in the former method, support vector classification algorithm was used to assess the level of coal mine essential safety management. According to the advantages of the global search capability of the genetic algorithm, support vector classification parameters optimization method was proposed based on genetic algorithm, and genetic algorithm-support vector classification model of coal mine essential safety management assessment was established. Learning samples were constructed on the basis of former data of mine essential safety management evaluation. The test results show that the genetic algorithm-support vector classification model has higher evaluation accuracy and good generalization ability, and the advantage of no need for artificial setting of index weight and absence of the subjective factors influence to evaluation results. 展开更多
关键词 mine safety essential safety management comprehensive assessment support vector classification genetic algorithm
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Soft Computing Based Discriminator Model for Glaucoma Diagnosis
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作者 Anisha Rebinth S.Mohan Kumar 《Computer Systems Science & Engineering》 SCIE EI 2022年第9期867-880,共14页
In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Intere... In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI. 展开更多
关键词 GLAUCOMA support vector classification clustering technique spatial domain and frequency domain features
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