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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
https://www.researchgate.net/publication/3193152_Robust_linear_and_support_vector_regression
In this work, we propose novel robust regularized support vector regression models with asymmetric Huber and ε-insensitive Huber loss functions leading to strongly convex minimization problems in ...
https://dl.acm.org/citation.cfm?id=353268
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
https://www.sciencedirect.com/science/article/pii/S0377221705006892
3. Robust regression. The Support Vector regression (SVR) that we will establish uses the ε-insensitive loss function to find a function h that has at most ε deviation from the targets y i for all the training data , . Again, each data point in the input space is mapped into a higher dimensional feature space using a feature map Φ.Cited by: 112
https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
Regression Overview CLUSTERING CLASSIFICATION REGRESSION (THIS TALK) K-means •Decision tree •Linear Discriminant Analysis •Neural Networks •Support Vector Machines •Boosting •Linear Regression •Support Vector Regression Group data based on their characteristics Separate data based on their labels Find a model that can explain
https://www.sciencedirect.com/science/article/pii/S0165168419305006
In this paper, we propose a novel classifier called robust weighted linear loss twin multi-class support vector regression (WLT-KSVC). This classifier aims to overcome two drawbacks of LST-KSVC. First, by introducing the weighted linear loss to the rest class samples, WLT-KSVC reduces the influence of the outliers for the rest class samples and lowers their misclassification probability.Author: Wenwen Qiang, Jinxin Zhang, Ling Zhen, Ling Jing
https://link.springer.com/article/10.1057/jors.2015.32
Dec 09, 2015 · Support 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... A robust support vector regression with a linear-log concave loss function …Cited by: 1
https://www.researchgate.net/publication/286490758_A_robust_support_vector_regression_with_a_linear-log_concave_loss_function
PDF Support vector regression (SVR) is one of the most popular nonlinear regression techniques with the aim to approximate a nonlinear system with a... Find, read and cite all the research you ...
http://www.support-vector-machines.org/SVM_regression.html
SVM, support vector machines, SVMC, support vector machines classification, SVMR, support vector machines regression, kernel, machine learning, pattern recognition ...
https://ieeexplore.ieee.org/document/6287596/
Aug 27, 2012 · Robust Support Vector Regression for Uncertain Input and Output Data Abstract: In this paper, a robust support vector regression (RSVR) method with uncertain input and output data is studied. First, the data uncertainties are investigated under a stochastic framework and two linear robust formulations are derived.Cited by: 35
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