Robust Support Vector Regression For Uncertain Input And Output Data

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Robust Support Vector Regression for Uncertain Input and ...

    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

(PDF) Robust Support Vector Regression for Uncertain Input ...

    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 ...

Robust support vector regression for uncertain input and ...

    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

Support vector interval regression machine for crisp input ...

    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. ...

Identification of robust Gaussian Process Regression with ...

    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

Support vector interval regression machine for crisp input ...

    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 …

Robust stochastic configuration networks with maximum ...

    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

Robust stochastic configuration networks with kernel ...

    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

Support vector regression with noisy data: A second order ...

    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 ...

Incremental Support Vector Learning for Ordinal Regression

    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 ...

Robust Support Vector Regression for Uncertain Input and ...

    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

(PDF) Robust Support Vector Regression for Uncertain Input ...

    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 ...

Robust support vector regression for uncertain input and ...

    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

Support vector interval regression machine for crisp input ...

    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. ...

Identification of robust Gaussian Process Regression with ...

    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

Support vector interval regression machine for crisp input ...

    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 …

Robust stochastic configuration networks with maximum ...

    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

Robust stochastic configuration networks with kernel ...

    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

Support vector regression with noisy data: A second order ...

    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 ...

Incremental Support Vector Learning for Ordinal Regression

    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 ...

Robust Support Vector Regression for Uncertain Input and ...

    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

(PDF) Robust Support Vector Regression for Uncertain Input ...

    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 ...

Robust support vector regression for uncertain input and ...

    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

Robust Support Vector Regression with Generalized Loss ...

    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

Support vector interval regression machine for crisp input ...

    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. ...

Identification of robust Gaussian Process Regression with ...

    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

Support vector interval regression machine for crisp input ...

    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 …

Support Vector Classification with Input Data Uncertainty

    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).

Robust Support Vector Regression with Generalized Loss ...

    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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