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
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient and interpretable fuzzy classifiers from data with support vector learning
Intelligent Data Analysis
TS-fuzzy system-based support vector regression
Fuzzy Sets and 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
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
TSK-fuzzy modeling based on ϵ-insensitive learning
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This paper extends the previous work in the connection between fuzzy classifiers and kernel machines [2] to a general case. In [2], all membership functions for the same input variable are generated from location transformation of a reference function. A translation invariant kernel is constructed from reference functions. The kernel is a Mercer kernel if the reference functions are positive definite. A support vector learning approach for positive definite fuzzy classifiers PDFCs was proposed. In this paper, a smooth support vector learning algorithm for fuzzy rule-based classification systems is proposed. The smooth support vector machine SSVM is capable of generating nonlinear separating surfaces using arbitrary kernels. The positive definiteness requirement on reference functions is relaxed. A fuzzy classifier using arbitrary reference functions can be built from the training samples based on an SSVM. The resulting fuzzy classifier is called standard binary fuzzy classifier SBFC. Fuzzy rules are extracted with each rule given by a training sample. The reduced kernel technique is introduced to simplify the decision function of the SBFC and to reduce the number of fuzzy rules. Finally, the performance of SBFCs with different reference functions is illustrated by experimental results. Comparisons with PDFCs are also provided.