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https://www.sciencedirect.com/science/article/pii/S0893608008001871
As for regression domain, there exist fewer letters about primal optimizations. Bo, Wang, and Jiao (2007) presented a recursive finite Newton method for nonlinear support vector regression in the primal and revealed that it is comparable with dual optimizing methods like LIBSVM 2.82.Cited by: 30
https://www.researchgate.net/publication/23293918_Robust_support_vector_regression_in_the_primal
This paper proposes a robust support vector regression based on a generalized non-convex loss function with flexible slope and margin. The robust model is more flexible for regression estimation.
https://www.researchgate.net/publication/326081364_Robust_Support_Vector_Regression_in_Primal_with_Asymmetric_Huber_Loss
Support vector regression (SVR) method becomes the state of the art machine learning method for data regression due to its excellent generalization performance on many real-world problems.
https://link.springer.com/article/10.1007/s11063-018-9875-8
Jul 06, 2018 · As real world data sets in general contain noise, construction of robust regression learning models to fit data with noise is an important and challenging research problem. It is all the more difficult to learn regression function with good generalization performance for input samples corrupted by asymmetric noise and outliers. In this work, we propose novel robust regularized support vector ...Cited by: 2
https://www.researchgate.net/publication/326081348_On_robust_twin_support_vector_regression_in_primal_using_squared_pinball_loss
On robust twin support vector regression in primal using squared pinball loss Article in Journal of Intelligent and Fuzzy Systems 35(5) · June 2018 with 58 Reads How we measure 'reads'
https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169807
Nov 20, 2018 · 2.1 Support vector regression. For a training set given and correspondingly to obtaining a nonlinear regression function, it is desired that the training data will be mapped into a higher dimensional feature space via a nonlinear mapping φ (.) [] and a linear regressor will be constructed in the feature space in which the resulting regression function will made as flat as possible.Cited by: 2
https://ieeexplore.ieee.org/document/877518/
Robust linear and support vector regression Abstract: The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear and nonlinear support vector estimators ...Cited by: 180
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