Structure identification of fuzzy model
Fuzzy Sets and Systems
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
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
Neuro-fuzzy system with learning tolerant to imprecision
Fuzzy Sets and Systems - Theme: Learning and modeling
The Journal of Machine Learning Research
Towards a robust fuzzy clustering
Fuzzy Sets and Systems - Data analysis
Kernel independent component analysis
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
An empirical risk functional to improve learning in a neuro-fuzzy classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Nonlinear blind source separation using kernels
IEEE Transactions on Neural Networks
A support vector machine formulation to PCA analysis and its kernel version
IEEE Transactions on Neural Networks
The theoretical foundations of statistical learning theory based on fuzzy number samples
Information Sciences: an International Journal
Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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Initially, the idea of approximate reasoning using generalized modus ponens and a fuzzy implication is recalled. Next, a fuzzy system based on logical interpretation of if-then rules and with parametric conclusions is presented. Then, it is shown that global and local @e-insensitive learning of the above fuzzy system may be presented as the learning of a support vector regression machine with a special type of a kernel matrix obtained from clustering. The kernel matrix may be interpreted in terms of linguistic values based on the premises of if-then rules. A new method of obtaining a fuzzy system by means of a support vector machine (SVM) with a data-dependent kernel matrix is introduced. This paper contains examples of a SVM used to design fuzzy models of real-life data. Simulation results show an improvement in the generalization ability of a fuzzy system learned by the new method compared with traditional learning methods.