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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...
P. Zhong, "Training robust support vector regression with smooth non-convex loss function", Optimization Methods and Software, vol. 6, no. 27, (2012).P. Zhong, "Training robust support vector regression with smooth non-convex loss function," Optimization Methods and Software, vol. 27, no. ...
In order to overcome the two drawbacks of LST-KSVC, this paper proposes a new classifier for multi-class classification called robust weighted linear loss twin multi-class support vector regression (WLT-KSVC). First, we punish the rest class samples by a weighted linear loss to make them lie...
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....
向量机回归(1eastsquaressupportvectorregression,LSSVR)建模易受离群点的影响.针对这一问题,结合鲁棒学习算 法(robustlearningalgorithm,PLEA),本文提出了一种在线鲁棒最小二乘支持向量机回归建模方法.该方法首先利 用LSSVR模型对过程输出进行预测,与真实输出相比较得到预测误差;然后利用RLA方法训练LSSVR模型的权值, ...
spectively, where n is the sample size, and d is the dimensionality of the input space. In this section, we first review the method of SVM and then introduce the RSVM. 2.1 The Support Vector Machine For illustration, we first briefly describe the linear binary SVM. Let y ∈ {±...
As a consequence of the previous properties, ρ should be preferentially convex, although this is not needed for successful use and implementation of the estimator. The convexity of ρ guarantee uniqueness of the obtained solution (global optimal) for problems described by linear models (Huber, 1981...
Various classification methods, such as support vector machine (Avidan 2004), multiple instance boosting (Babenko et al. 2011), and linear regression (Henriques et al. 2012) have been employed in constructing learning models, exploiting the discriminatory information between target region and its ...
Multi-kernel learning is trained in the scheme of least squares support vector regression (LS-SVR), developed from structural risk minimization principle [15], for balancing fitting error and model complexity [16] over the measured auto-calibrating signal (ACS) data. Both phantom and in vivo ...