Matrix analysis
The nature of statistical learning theory
The nature of statistical learning theory
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Fuzzy Theory Systems: Techniques and Applications
Fuzzy Theory Systems: Techniques and Applications
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A tutorial on support vector regression
Statistics and Computing
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
TS-fuzzy system-based support vector regression
Fuzzy Sets and Systems
A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling
IEEE Transactions on Fuzzy Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Fuzzy Systems
TSK-fuzzy modeling based on ϵ-insensitive learning
IEEE Transactions on Fuzzy Systems
Active set support vector regression
IEEE Transactions on Neural Networks
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
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This paper establishes a connection between Takagi-Sugeno TS fuzzy systems and ε-insensitive smooth support vector regression ε-SSVR, a smooth strategy for solving ε-SVR. In previous ε-SVR-based fuzzy models, the form of membership functions is restricted by the Mercer condition. The ε-SSVR formulation puts no restrictions on the kernel. Therefore, the proposed ε-SSVR-based TS-fuzzy modeling method relaxes the restriction on membership functions. By applying the reduced kernel technique, the number of fuzzy rules is reduced without scarifying the generalization ability. The computational complexity is also reduced by the reduced kernel technique. The performance of our method is illustrated by extensive experiments and comparisons.