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https://ieeexplore.ieee.org/document/6287596/
Aug 27, 2012 · 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. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective.Cited by: 35
https://www.researchgate.net/publication/260354203_Robust_Support_Vector_Regression_for_Uncertain_Input_and_Output_Data
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 ...
https://www.ncbi.nlm.nih.gov/pubmed/24808065
Robust support vector regression for uncertain input and output data. Huang G, Song S, Wu C, You K. 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
https://www.researchgate.net/publication/220527772_Support_vector_interval_regression_machine_for_crisp_input_and_output_data
Support vector interval regression machine (SVIRM) is designed for crisp input and output data [3]. SVIRM is robust in the sense that outliers do not affect the resulting interval regression. ...
https://www.sciencedirect.com/science/article/pii/S0169743919300450
Their solution separately estimated the input probability density with a Gaussian mixture model (GMM) and the conditional probability density with a feed-forward network. Also, a new robust support vector regression model with uncertain input and output for both …Cited by: 1
https://www.sciencedirect.com/science/article/pii/S0165011405004689#!
Support vector regression (SVR) has been very successful in function estimation problems for crisp data. In this paper, we propose a robust method to …
https://www.sciencedirect.com/science/article/pii/S0020025518307278
Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression. Author links open overlay panel ... Chuang C.C., Su S.F., Tsong J., Hsiao C.C.Robust support vector regression networks for function approximation ... M. LiRobust stochastic configuration networks with kernel density estimation for ...Cited by: 5
https://www.sciencedirect.com/science/article/pii/S0020025517307636
Robust stochastic configuration networks with kernel density estimation for uncertain data regression. ... some results on robust data regression using support vector machine (SVM) ... Both the input and output values are normalized into [0,1].Cited by: 25
https://www.researchgate.net/publication/232952115_Support_vector_regression_with_noisy_data_A_second_order_cone_programming_approach
A robust convex optimization approach is proposed for support vector regression (SVR) with noisy input data. The data points are assumed to be uncertain, but bounded within given hyper-spheres of ...
https://www.researchgate.net/publication/264831876_Incremental_Support_Vector_Learning_for_Ordinal_Regression
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 ...
https://ieeexplore.ieee.org/document/6287596/
Aug 27, 2012 · 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. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective.Cited by: 35
https://www.researchgate.net/publication/260354203_Robust_Support_Vector_Regression_for_Uncertain_Input_and_Output_Data
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 ...
https://www.ncbi.nlm.nih.gov/pubmed/24808065
Robust support vector regression for uncertain input and output data. Huang G, Song S, Wu C, You K. 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
https://www.researchgate.net/publication/220527772_Support_vector_interval_regression_machine_for_crisp_input_and_output_data
Support vector interval regression machine (SVIRM) is designed for crisp input and output data [3]. SVIRM is robust in the sense that outliers do not affect the resulting interval regression. ...
https://www.sciencedirect.com/science/article/pii/S0169743919300450
Their solution separately estimated the input probability density with a Gaussian mixture model (GMM) and the conditional probability density with a feed-forward network. Also, a new robust support vector regression model with uncertain input and output for both …Cited by: 1
https://www.sciencedirect.com/science/article/pii/S0165011405004689#!
Support vector regression (SVR) has been very successful in function estimation problems for crisp data. In this paper, we propose a robust method to …
https://www.sciencedirect.com/science/article/pii/S0020025518307278
Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression. Author links open ... , and robust support vector regression . An alternative ... (por) for training while the instances corresponding to Mathematics (mat) for testing. Then, both the input and output values for training and testing ...Cited by: 5
https://www.sciencedirect.com/science/article/pii/S0020025517307636
Robust stochastic configuration networks with kernel density estimation for uncertain data regression. ... some results on robust data regression using support vector machine (SVM) ... Both the input and output values are normalized into [0,1].Cited by: 25
https://www.researchgate.net/publication/232952115_Support_vector_regression_with_noisy_data_A_second_order_cone_programming_approach
A robust convex optimization approach is proposed for support vector regression (SVR) with noisy input data. The data points are assumed to be uncertain, but bounded within given hyper-spheres of ...
https://www.researchgate.net/publication/264831876_Incremental_Support_Vector_Learning_for_Ordinal_Regression
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 ...
https://ieeexplore.ieee.org/document/6287596/
Aug 27, 2012 · 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. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective.Cited by: 35
https://www.researchgate.net/publication/260354203_Robust_Support_Vector_Regression_for_Uncertain_Input_and_Output_Data
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 ...
https://www.ncbi.nlm.nih.gov/pubmed/24808065
Robust support vector regression for uncertain input and output data. Huang G, Song S, Wu C, You K. 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
https://link.springer.com/article/10.1007%2Fs11063-013-9336-3
Jan 01, 2014 · Huang G, Song S, Wu C, You K (2012) Robust support vector regression for uncertain input and output data. IEEE Trans Neural Netw …Cited by: 12
https://www.researchgate.net/publication/220527772_Support_vector_interval_regression_machine_for_crisp_input_and_output_data
Support vector interval regression machine (SVIRM) is designed for crisp input and output data [3]. SVIRM is robust in the sense that outliers do not affect the resulting interval regression. ...
https://www.sciencedirect.com/science/article/pii/S0169743919300450
Their solution separately estimated the input probability density with a Gaussian mixture model (GMM) and the conditional probability density with a feed-forward network. Also, a new robust support vector regression model with uncertain input and output for both …Cited by: 2
https://www.sciencedirect.com/science/article/pii/S0165011405004689#!
Support vector regression (SVR) has been very successful in function estimation problems for crisp data. In this paper, we propose a robust method to …
http://papers.nips.cc/paper/2743-support-vector-classification-with-input-data-uncertainty.pdf
support vector machines in the rest of the paper. 3 Total support vector classification Our formulation of support vector classification with uncertain input data is motivated by the total least squares regression method that can be derived from the statistical model (3).
https://dl.acm.org/citation.cfm?id=2720583
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 margin. The robust model is more flexible for regression estimation. Meanwhile, it has strong ability of suppressing the impact of outliers.Cited by: 12
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