Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Approximation capabilities of multilayer feedforward networks
Neural Networks
Fuzzy regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Robust interval regression analysis using neural networks
Fuzzy Sets and Systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fuzzy regression wiht radial basis function network
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
Protein homology detection using string alignment kernels
Bioinformatics
Kernel methods for predicting protein--protein interactions
Bioinformatics
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Neural Computation
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
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
Population-based neighborhood search for job shop scheduling with interval processing time
Computers and Industrial Engineering
A Midpoint--Radius approach to regression with interval data
International Journal of Approximate Reasoning
Interval regression by tolerance analysis approach
Fuzzy Sets and Systems
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
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Support vector machines (SVMs) have been very successful in pattern classification and function estimation problems for crisp data. In this paper, the v-support vector interval regression network (v-SVIRN) is proposed to evaluate interval linear and nonlinear regression models for crisp input and output data. As it is difficult to select an appropriate value of the insensitive tube width in @e-support vector regression network, the proposed v-SVIRN alleviates this problem by utilizing a new parametric-insensitive loss function. The proposed v-SVIRN automatically adjusts a flexible parametric-insensitive zone of arbitrary shape and minimal size to include the given data. Besides, the proposed method can achieve automatic accuracy control in the interval regression analysis task. For a priori chosen v, at most a fraction v of the data points lie outside the interval model constructed by the proposed v-SVIRN. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. Hence, the selection of v is more intuitive. Moreover, the proposed algorithm here is a model-free method in the sense that we do not have to assume the underlying model function. Experimental results are then presented which show the proposed v-SVIRN is useful in practice, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x.