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Consensus Proximal Support Vector Machine for classification problems with Sparse Solutions

Journal of the Operations Research Society of China 2014年第1期2卷 57-74页

作者： Yan-Qin Bai Yan-Jun Shen Kai-Ji Shen Department of Mathematics Shanghai UniversityShanghai 200444China

classification problem is the central problem in machine *** vector machines(SVMs)are supervised learning models with associated learning algorithms and are used for classification in machine *** this paper,we establish two consensus proximal support vector machines(PSVMs)models,based on methods for binary *** first one is to separate the objective functions into individual convex functions by using the number of the sample points of the training *** constraints contain two types of the equations with global variables and local variables corresponding to the consensus points and sample points,*** get more sparse solutions,the second one is l1–l2 consensus PSVMs in which the objective function contains an■1-norm term and an■2-norm term which is responsible for the good classification performance while■1-norm term plays an important role in finding the sparse *** consensus PSVMs are solved by the alternating direction method of ***,they are implemented by the real-world data taken from the University of California,Irvine Machine Learning Repository(UCI Repository)and are compared with the existed models such as■1-PSVM,■p-PSVM,GEPSVM,PSVM,and *** results show that our models outperform others with the classification accuracy and the sparse solutions.

关键词： classification problems Support vector machine Proximal support vector machine Consensus Alternating direction method of multipliers

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Machine Learning Enhanced Boundary Element Method:Prediction of Gaussian Quadrature Points

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 445-464页

作者： Ruhui Cheng Xiaomeng Yin Leilei Chen College of Architecture and Civil Engineering Xinyang Normal UniversityXinyang464000China College of Intelligent Construction Wuchang University of TechnologyWuhan430223China School of Architectural Engineering Huanghuai UniversityZhumadian463000China

This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods.A model based on the neural network multi-classification algorithmis constructed to find the minimum number of Gaussian quadrature points satisfying the given *** constructed model is trained by using a large amount of data calculated in the traditional boundary element method and the optimal network architecture is *** two-dimensional potential problem of a circular structure is tested and analyzed based on the determined model,and the accuracy of the model is about 90%.Finally,by incorporating the predicted Gaussian quadrature points into the boundary element analysis,we find that the numerical solution and the analytical solution are in good agreement,which verifies the robustness of the proposed method.

关键词： Machine learning Boundary element method Gaussian quadrature points classification problems

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Alternating Direction Method of Multipliers for l_(1)-l_(2)-Regularized Logistic Regression Model

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Journal of the Operations Research Society of China 2016年第2期4卷 243-253页

作者： Yan-Qin Bai Kai-Ji Shen Department of Mathematics Shanghai UniversityShanghai 200444China

Logistic regression has been proved as a promising method for machine learning,which focuses on the problem of *** this paper,we present anl_(1)-l_(2)-regularized logistic regression model,where thel1-norm is responsible for yielding a sparse logistic regression classifier and thel_(2)-norm for keeping betlter classification *** solve thel_(1)-l_(2)-regularized logistic regression model,we develop an alternating direction method of multipliers with embedding limitedlBroyden-Fletcher-Goldfarb-Shanno(L-BFGS)***,we implement our model for binary classification problems by using real data examples selected from the University of California,Irvine Machines Learning Repository(UCI Repository).We compare our numerical results with those obtained by the well-known LIBSVM and SVM-Light *** numerical results show that ourl_(1)-l_(2)-regularized logisltic regression model achieves better classification and less CPU Time.

关键词： classification problems Logistic regression model Sparsity Alternating direction method of multipliers

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Online Sequential Double Parallel Extreme Learning Machine for classifications

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Journal of Mathematical Research with Applications 2016年第5期36卷 621-630页

作者： Mingchen YAO Chao ZHANG Wei WU School of Mathematical Sciences Dalian University of Technology

Double parallel forward neural network(DPFNN) model is a mixture structure of single-layer perception and single-hidden-layer forward neural network(SLFN).In this paper,by making use of the idea of online sequential extreme learning machine(OS-ELM) on DPFNN,we derive the online sequential double parallel extreme learning machine algorithm(OS-DPELM).Compared to other similar algorithms,our algorithms can achieve approximate learning performance with fewer numbers of hidden units,as well as the parameters to be *** experimental results show that the proposed algorithm has good generalization performance for real world classification problems,and thus can be a necessary and beneficial complement to OS-ELM.

关键词： double parallel forward neural network perception extreme learning machine classification problems

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