Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Support vector interval regression machine for crisp input and output data
Fuzzy Sets and Systems
Interval regression analysis by quadratic programming approach
IEEE Transactions on Fuzzy Systems
A class of linear interval programming problems and its application to portfolio selection
IEEE Transactions on Fuzzy Systems
Interval regression analysis using quadratic loss support vector machine
IEEE Transactions on Fuzzy Systems
Model complexity control for regression using VC generalization bounds
IEEE Transactions on Neural Networks
Multiple model regression estimation
IEEE Transactions on Neural Networks
Imprecise regression based on possibilistic likelihood
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
A Midpoint--Radius approach to regression with interval data
International Journal of Approximate Reasoning
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In an asymmetrical interval data set, the error ranges of the upper and lower interval ends are different. This situation is common in practice because of the usual presence of uncertain influences. In prior ''crisp input and interval output'' regression analysis, a crude symmetrical estimation is obtained, and the asymmetrical character of training data cannot be depicted exactly. In this paper, an asymmetrical interval data analysis is proposed for the first time. The two interval ends are studied independently, and a set of regression models and @e-insensitive functions are proposed to strengthen the description of the interval ends. The support vector machine (SVM) is imported into this approach (for its model-free character in nonlinear regression) and further extended by @e-insensitive functions to the extended @e-SVM. A robust algorithm is presented to eliminate the effect of outliers. Experiments are then presented to verify the quality of performance of the extended @e-SVM. Advantages over other approaches are considered in the conclusion.