The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will resu...The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.展开更多
In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is...In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is revisited. A case study of mechanical twinning of HfV2+Nb, (cubic) Laves phase, is presented and the synchroshear of selected atomic layers is proposed to explain the physical process of twin formation. If the twins form in this way, then long shear vectors and / or atomicshuffles are not really necessary.展开更多
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
With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly af...With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly affect the performance of the entire network. Traditional processing methods include classification models such as fully connected network models and support vector machines. In order to solve the problem that the traditional convolutional neural network is prone to over-fitting for the classification of small samples, a CNN-TWSVM hybrid model was proposed by fusing the twin support vector machine (TWSVM) with higher computational efficiency as the CNN classifier, and it was applied to the traffic sign recognition task. In order to improve the generalization ability of the model, the wavelet kernel function is introduced to deal with the nonlinear classification task. The method uses the network initialized from the ImageNet dataset to fine-tune the specific domain and intercept the inner layer of the network to extract the high abstract features of the traffic sign image. Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. On GTSRB and BELGIUMTS datasets, the validity and generalization ability of the improved model is verified by comparing with different kernel functions and different SVM classifiers.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
Using the twin shear stress yield criterion, the surface integral of the co-line vectors, and the integration depending on upper limit, Kobayashi's three-dimensional velocity field of rolling was analyzed and an anal...Using the twin shear stress yield criterion, the surface integral of the co-line vectors, and the integration depending on upper limit, Kobayashi's three-dimensional velocity field of rolling was analyzed and an analytical expression of rolling torque and single force was obtained. Through redoing the same experiment of rolling pure lead as Sims, the calculated results by the above expression were compared with those of Kobayashi and Sims formulae. The results show that the twin shear stress yield criterion is available for rolling analysis and the calculated results by the new formula are a little higher than those by Kobayashi and Sims ones if the reduction ratio is less than 30%.展开更多
Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonab...Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.展开更多
This review chronicles the development of the plant binary vectors of Ti plasmid in Agrobacterium tumefaciens during the last 30 years. A binary vector strategy was designed in 1983 to separate the T-DNA region in a s...This review chronicles the development of the plant binary vectors of Ti plasmid in Agrobacterium tumefaciens during the last 30 years. A binary vector strategy was designed in 1983 to separate the T-DNA region in a small plasmid from the virulence genes in avirulent T-DNA-less Ti plasmid. The small plant vectors with the T-DNA region have been simply now called binary Ti vectors. A binary Ti vector consist of a broad host-range replicon for propagation in A. tumeraciens, an antibiotic resistance gene for bacterial selection and the T-DNA region that would be transferred to the plant genome via the bacterial virulence machinery. The T-DNA region delimited by the right and left border sequences contains an antibiotic resistance gene for plant selection, reporter gene, and/or any genes of interest. The ColEI replicon was also added to the plasmid backbone to enhance the propagation in Escherichia coli. A general trend in the binary vector development has been to increase the plasmid stability during a long co-cultivation period of A. tumefaciens with the target host plant tissues. A second trend is to understand the molecular mechanism of broad host-range replication, and to use it to reduce the size of plasmid for ease in cloning and for higher plasmid yield in E. coli. The broad host-range replicon of VS1 was shown to be a choice of replicon over those of pRK2, pRi and pSA because of the superior stability and of small well-defined replicon. Newly developed plant binary vectors pLSU has the small size of plasmid backbone (4566 bp) consisting of VS1 replicon (2654 bp), ColE1 replicon (715 bp), a bacterial kanamycin (999 bp) or tetracycline resistance gene, and the T-DNA region (152 bp).展开更多
基金Hebei Province Key Research and Development Project(No.20313701D)Hebei Province Key Research and Development Project(No.19210404D)+13 种基金Mobile computing and universal equipment for the Beijing Key Laboratory Open Project,The National Social Science Fund of China(17AJL014)Beijing University of Posts and Telecommunications Construction of World-Class Disciplines and Characteristic Development Guidance Special Fund “Cultural Inheritance and Innovation”Project(No.505019221)National Natural Science Foundation of China(No.U1536112)National Natural Science Foundation of China(No.81673697)National Natural Science Foundation of China(61872046)The National Social Science Fund Key Project of China(No.17AJL014)“Blue Fire Project”(Huizhou)University of Technology Joint Innovation Project(CXZJHZ201729)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201902218004)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201902024006)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201901197007)Industry-University Cooperation Collaborative Education Project of the Ministry of Education(No.201901199005)The Ministry of Education Industry-University Cooperation Collaborative Education Project(No.201901197001)Shijiazhuang science and technology plan project(236240267A)Hebei Province key research and development plan project(20312701D)。
文摘The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.
文摘In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is revisited. A case study of mechanical twinning of HfV2+Nb, (cubic) Laves phase, is presented and the synchroshear of selected atomic layers is proposed to explain the physical process of twin formation. If the twins form in this way, then long shear vectors and / or atomicshuffles are not really necessary.
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With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly affect the performance of the entire network. Traditional processing methods include classification models such as fully connected network models and support vector machines. In order to solve the problem that the traditional convolutional neural network is prone to over-fitting for the classification of small samples, a CNN-TWSVM hybrid model was proposed by fusing the twin support vector machine (TWSVM) with higher computational efficiency as the CNN classifier, and it was applied to the traffic sign recognition task. In order to improve the generalization ability of the model, the wavelet kernel function is introduced to deal with the nonlinear classification task. The method uses the network initialized from the ImageNet dataset to fine-tune the specific domain and intercept the inner layer of the network to extract the high abstract features of the traffic sign image. Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. On GTSRB and BELGIUMTS datasets, the validity and generalization ability of the improved model is verified by comparing with different kernel functions and different SVM classifiers.
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.
基金ItemSponsored by National Natural Science Foundation of China (50474015)
文摘Using the twin shear stress yield criterion, the surface integral of the co-line vectors, and the integration depending on upper limit, Kobayashi's three-dimensional velocity field of rolling was analyzed and an analytical expression of rolling torque and single force was obtained. Through redoing the same experiment of rolling pure lead as Sims, the calculated results by the above expression were compared with those of Kobayashi and Sims formulae. The results show that the twin shear stress yield criterion is available for rolling analysis and the calculated results by the new formula are a little higher than those by Kobayashi and Sims ones if the reduction ratio is less than 30%.
基金Supported by the National Natural Science Foundation of China (No. 60771068)the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2007F248)
文摘Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.
文摘This review chronicles the development of the plant binary vectors of Ti plasmid in Agrobacterium tumefaciens during the last 30 years. A binary vector strategy was designed in 1983 to separate the T-DNA region in a small plasmid from the virulence genes in avirulent T-DNA-less Ti plasmid. The small plant vectors with the T-DNA region have been simply now called binary Ti vectors. A binary Ti vector consist of a broad host-range replicon for propagation in A. tumeraciens, an antibiotic resistance gene for bacterial selection and the T-DNA region that would be transferred to the plant genome via the bacterial virulence machinery. The T-DNA region delimited by the right and left border sequences contains an antibiotic resistance gene for plant selection, reporter gene, and/or any genes of interest. The ColEI replicon was also added to the plasmid backbone to enhance the propagation in Escherichia coli. A general trend in the binary vector development has been to increase the plasmid stability during a long co-cultivation period of A. tumefaciens with the target host plant tissues. A second trend is to understand the molecular mechanism of broad host-range replication, and to use it to reduce the size of plasmid for ease in cloning and for higher plasmid yield in E. coli. The broad host-range replicon of VS1 was shown to be a choice of replicon over those of pRK2, pRi and pSA because of the superior stability and of small well-defined replicon. Newly developed plant binary vectors pLSU has the small size of plasmid backbone (4566 bp) consisting of VS1 replicon (2654 bp), ColE1 replicon (715 bp), a bacterial kanamycin (999 bp) or tetracycline resistance gene, and the T-DNA region (152 bp).