Linear programming twin support vector regression

Tanveer, M.

doi:10.2298/fil1707123t

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…This leads to a sparse solution with significantly less computational time. In 2017, Tanveer [177] formulated 1-norm TSVR to improve robustness and sparsity in original TSVR. 1-norm TSVR has better accuracy, generalization, and less computational time than TSVR.…”

Section: Robust and Sparse Twin Support Vector Regressionmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Section: Robust and Sparse Twin Support Vector Regressionmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Liu Y proposed a geometric external climbing method based on inclusive normal cones for solving general linear programming problems of typical forms [8]. Tanveer M proposed a new linear programming formulation for dual support vector regression [9]. Pramanik S proposed a new concept of optimization problem under uncertainty and indeterminacy.…”

Section: Introductionmentioning

confidence: 99%

Deconstruction of Road Logistics Transportation Cost Management Evaluation Based on Optimal Solution of Linear Programming

Liu

2022

Mathematical Problems in Engineering

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Due to the rapid development of the logistics industry, the business volume of the road transportation industry is also growing rapidly and its transportation costs are also increasing. Therefore, it is particularly important to control the transportation cost. This paper aims to evaluate and analyze the cost management of road logistics transportation based on the optimal solution of linear programming. This paper introduces the basic concept of highway logistics in general and analyzes its components and characteristics. Road logistics transportation has the characteristics of fast speed, low investment, strong flexibility, and easy to adapt to local conditions and does not require much infrastructure. We then analyzed the general linear programming model, made a brief introduction to its mathematical model, and proposed the construction of the optimization model to explore the transportation model of production and sales balance. Finally, taking W logistics company as a case to analyze and discuss, the influence of the transportation route in transit on the transportation cost is studied. And its structure and the corresponding transportation cost of logistics enterprises are analyzed in detail. The experimental results of this paper show that by comparing the calculation results of the road segment method and the linear programming model, the total freight calculated by the segment method is 5610 yuan, while the total freight calculated by the linear programming method is 5490 yuan. Comparing the piecewise methods, it can be found that the linear programming method is more economical in transportation cost than the piecewise method. Therefore, when logistics companies optimize road transportation costs, it is more feasible and effective to use linear programming methods.

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“…Computational results show that TBSVM is not only faster but also shows better generalization. To improve the robustness and sparseness, recently, Tanveer [33][34][35] proposed novel linear programming formulation of 1-norm twin support vector machine for classification and regression problems, whose solution is obtained, by solving a pair of exterior penalty problems in the dual space as unconstrained optimization problems using Newton-Armijo algorithm.…”

Section: Introductionmentioning

confidence: 99%

Smooth twin support vector machines via unconstrained convex minimization

Tanveer

Kumar

2017

Filomat

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Twin support vector machine (TWSVM) exhibits fast training speed with better classification abilities compared with standard SVM. However, it suffers the following drawbacks: (i) the objective functions of TWSVM are comprised of empirical risk and thus may suffer from overfitting and suboptimal solution in some cases. (ii) a convex quadratic programming problems (QPPs) need to be solve, which is relatively complex to implement. To address these problems, we proposed two smoothing approaches for an implicit Lagrangian TWSVM classifiers by formulating a pair of unconstrained minimization problems in dual variables whose solutions will be obtained by solving two systems of linear equations rather than solving two QPPs in TWSVM. Our proposed formulation introduces regularization terms to each objective function with the idea of maximizing the margin. In addition, our proposed formulation becomes well-posed model due to this term, which introduces invertibility in the dual formulation. Moreover, the structural risk minimization principle is implemented in our formulation which embodies the essence of statistical learning theory. The experimental results on several benchmark datasets show better performance of the proposed approach over existing approaches in terms of estimation accuracy with less training time.

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Linear programming twin support vector regression

Cited by 11 publications

References 40 publications

Comprehensive Review On Twin Support Vector Machines

Comprehensive Review On Twin Support Vector Machines

Deconstruction of Road Logistics Transportation Cost Management Evaluation Based on Optimal Solution of Linear Programming

Smooth twin support vector machines via unconstrained convex minimization

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