A subjective approach for ranking fuzzy numbers
Fuzzy Sets and Systems
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Robust Classification for Imprecise Environments
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Regularization and statistical learning theory for data analysis
Computational Statistics & Data Analysis - Nonlinear methods and data mining
A robust minimax approach to classification
The Journal of Machine Learning Research
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Fuzzy one-class support vector machines
Fuzzy Sets and Systems
Probability Error in Global Optimal Hierarchical Classifier with Intuitionistic Fuzzy Observations
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Robust support vector machine training via convex outlier ablation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Classification error in Bayes multistage recognition task with fuzzy observations
Pattern Analysis & Applications
Robustness and Regularization of Support Vector Machines
The Journal of Machine Learning Research
An improved fuzzy support vector machine for credit rating
NPC'07 Proceedings of the 2007 IFIP international conference on Network and parallel computing
Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems
Neural Processing Letters
IEEE Transactions on Neural Networks
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A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.