R. Musicant, "Robust linear and support vector re- gression," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 9, pp. 950-955, 2000.O.L. Mangasarian and D.R. Musicant, "Robust Linear and Support Vector Regression," IEEE Trans. Pattern Analysis and Machine Intelligence, vol....
Robust Linear and Support Vector Regression. Focuses on a study which described the modeling of robust Huber M-estimator by an easily solvable simple convex quadratic program for both linear and nonli... Mangasarian,Olvi,L.,... - 《IEEE Transactions on Pattern Analysis & Machine Intelligence》...
linear-log concave loss functionvirtual metrologySupport vector regression (SVR) is one of the most popular nonlinear regression techniques with the aim to approximate a nonlinear system with a good generalization capability. However, SVR has a major drawback in that it is sensitive to the presence...
Robust method based on optimized support vector regression for modeling of asphaltene precipitation 来自 dx.doi.org 喜欢 0 阅读量: 32 作者:HR Ansari,A Gholami 摘要: •In this study, asphaltene precipitationswere predictedfrom titration data.•Optimized SVR was employed for this prediction.•...
论文关键词:Support vector regression,Twin support vector machines,Second-order cone programming,Robust optimization论文评审过程:Received 9 September 2017, Revised 1 April 2018, Accepted 2 April 2018, Available online 3 April 2018, Version of Record 12 May 2018....
Summary: In this research, a robust optimization approach applied to support vector regression (SVR) is investigated. A novel kernel based-method is developed to address the problem of data uncertainty where each data point is inside a sphere. The model is called robust SVR. Computational results...
RLS-SVR iteratively builds the regression function by solving a set of linear equations at one time. Numerical experiments on both artificial datasets and benchmark datasets reveal the efficiency of the proposed method. The rest of this paper is organized as follows. In Section 2, we present a...
Quantile Regression is more powerful... D Ramdani,AV Witteloostuijn - University of Antwerp, Faculty of Applied Economics 被引量: 107发表: 2009年 On the Dual Formulation of Regularized Linear Systems with Convex Risks convex dualitylinear modellogistic regressionregulationsupport vector machineIn this...
The RMSProp technique is utilized to solve both linear and nonlinear QTLS. Extensive experiments indicate the effectiveness of QTLS in addressing binary classification problems. Introduction Support vector machines (SVMs) are powerful computational models in the field of machine learning [1]. Compared ...
Motivated by the presence of uncertainty in real data, in this research we investigate a robust optimization approach applied to multiclass support vector machines (SVMs) and support vector regression. Two new kernel based-methods are developed to address data with uncertainty where each data point ...