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https://www.researchgate.net/publication/5606724_Robust_support_vector_regression_networks_for_function_approximation_with_outliers
Robust support vector regression networks for function approximation with outliers. ... a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to ...
https://www.sciencedirect.com/science/article/pii/S1568494619302534
Further, two robust SVM frameworks are presented to handle robust classification and regression problems by applying L q-loss to support vector machine, respectively. Last but not least, we demonstrate that the proposed classification framework satisfies Bayes’ optimal decision rule.Cited by: 2
https://www.sciencedirect.com/science/article/pii/S0950705118301709
Twin Support Vector Regression is an effective machine learning strategy, which splits the predictive task into two small problems, gaining in both efficiency and predictive performance. In this paper, a novel extension for twin Support Vector Regression is presented.Cited by: 6
https://www.sciencedirect.com/science/article/pii/S0957417409011282
In this paper, we utilize two ε-insensitive loss functions to construct a non-convex loss function.Based on this non-convex loss function, a robust truncated support vector regression (TSVR) is proposed. In order to solve the TSVR, the concave–convex procedure is used to circumvent this problem though transforming the non-convex problem to a sequence of convex ones.Cited by: 8
https://www.sciencedirect.com/science/article/pii/S0893608008001871
1. Introduction. Support vector machine (SVM) (Burges, 1998, Cristianini and Shawe-Taylor, 2000, Schölkopf and Smola, 2002, Vapnik, 1995) has been an elegant and powerful tool for classification and regression over the past decade as a modern machine learning approach, which is based on the structural risk minimization principle and enjoys excellent successes in many real-world applications.Cited by: 30
https://www.sciencedirect.com/science/article/pii/S0096300317300887
L p-norm least squares support vector regression (L p-LSSVR) is proposed for feature selection in regression.. Using the absolute constraint and the L p-norm regularization term, L p-LSSVR performs robust against outliers.. L p-LSSVR ensures the useful features to be selected based on theoretical analysis.. L p-LSSVR only solves a series of linear equations, leading to fast training speed.Cited by: 11
https://www.researchgate.net/publication/3193152_Robust_linear_and_support_vector_regression
Robust linear and support vector regression. ... we propose novel robust regularized support vector regression models with asymmetric Huber and ε-insensitive Huber loss functions leading to ...
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 ...
https://www.researchgate.net/publication/262569252_A_robust_least_squares_support_vector_machine_for_regression_and_classification_with_noise
A robust least squares support vector machine for regression and classification with noise Article (PDF Available) in Neurocomputing 140:41–52 · September 2014 with 596 Reads How we measure 'reads'
https://www.svm-tutorial.com/2014/10/support-vector-regression-r/
In this article I will show how to use R to perform a Support Vector Regression. We will first do a simple linear regression, then move to the Support Vector Regression so that you can see how the two behave with the same data.
https://www.hindawi.com/journals/mpe/2014/373571/
Spheroid disturbance of input data brings great challenges to support vector regression; thus it is essential to study the robust regression model. This paper is dedicated to establish a robust regression model which makes the regression function robust against disturbance of data and system parameter. Firstly, two theorems have been given to show that the robust linear ε-support vector ...
http://people.ece.umn.edu/users/cherkass/N2002-SI-SVM-13-whole.doc
Support Vector Regression and SVM Parameter Selection. In regression formulation, the goal is to estimate an unknown continuous-valued function based on a finite number set of noisy samples, where d-dimensional inputand the output . Assumed statistical model for data generation has the following form: ... Robust Parameter Choice in Support ...
http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
Robust Anomaly Detection Using Support Vector Machines Wenjie Hu Yihua Liao V. Rao Vemuri Department of Applied Science Department of Computer Science Department of Applied Science University of California, Davis University of California, Davis University of California, Davis [email protected] [email protected] [email protected]
https://pdfs.semanticscholar.org/4ee5/e4d114d71c68f1f444da0698f7437e880c24.pdf
Robust Support Vector Regression Networks for Function Approximation With Outliers Chen-Chia Chuang, Shun-Feng Su, Member, IEEE, Jin-Tsong Jeng, Member, IEEE, and Chih-Ching Hsiao Abstract— Support vector regression (SVR) employs the sup-port vector machine (SVM) to tackle problems of function approx-imation and regression estimation.
https://link.springer.com/article/10.1007%2Fs00521-019-04625-8
Dec 13, 2019 · Since Huber function has the property that inputs with large deviations of misfit are penalized linearly and small errors are squared, we present novel robust regularized twin support vector machines for data regression based on Huber and ε
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://dl.acm.org/doi/10.1007/s11063-013-9336-3
The classical support vector machine (SVM) is sensitive to outliers. This paper proposes a robust support vector regression based on a generalized non-convex loss function with flexible slope and m...
https://stats.stackexchange.com/questions/26305/robust-support-vector-regression-robust-to-outliers
I've been reading/looking around for literature on support vector regressions that are relatively robust to outliers. I understand that standard SVRs can be significantly influenced by a few large outliers. From what I've read (and I'm no academic to be sure), there appears to be a number of different approaches.
https://pdfs.semanticscholar.org/f3b2/2e5f5c5c5ed8a337e61f41d28f43f3c7cf98.pdf
In the interest of deriving regressor that is robust to outliers, we propose a support vector regression (SVR) based on non-convex quadratic insensitive loss function with flexible coefficient and margin. The proposed loss function can be approximated by a difference of convex functions (DC). The resultant optimization is a DC program.
https://www.mathworks.com/help/stats/robustfit.html
b = robustfit(X,y) returns a (p + 1)-by-1 vector b of coefficient estimates for a robust multilinear regression of the responses in y on the predictors in X. X is an n-by-p matrix of p predictors at each of n observations. y is an n-by-1 vector of observed responses. By default, the algorithm uses iteratively reweighted least squares with a ...
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.
https://isn.ucsd.edu/pub/papers/jmlr07_gini.pdf
Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression Shantanu Chakrabartty [email protected] Department of Electrical and Computer Engineering Michigan State University East Lansing, MI 48824, USA Gert Cauwenberghs [email protected] Division of Biological Sciences University of California San Diego
https://polystat.blogspot.com/2016/07/svms-are-not-robust-to-outliers-but.html
SVM is NOT robust to outliers but median regression is This text is a bit technical. It is the result of a discussion with Andrea Lodi concerning the robustness of the support vector machines (SVM) the famous and the widely-used classifier in Machine Learning.
http://downloads.hindawi.com/journals/mpe/2014/373571.pdf
disturbance of data and system parameter. Firstly, two theorems have been given to show that the robust linear -support vector regression problem could be settled by solving the dual problems. Secondly, it has been focused on the development of robust
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