Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy Sets and Systems
Pairwise classification and support vector machines
Advances in kernel methods
Template-based procedures for neural network interpretation
Neural Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Readings in Fuzzy Sets for Intelligent Systems
Readings in Fuzzy Sets for Intelligent Systems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Fuzzy Classifier Design
Full reinforcement operators in aggregation techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Interpretation of artificial neural networks by means of fuzzy rules
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Are artificial neural networks white boxes?
IEEE Transactions on Neural Networks
The theoretical foundations of statistical learning theory based on fuzzy number samples
Information Sciences: an International Journal
The theoretical fundamentals of learning theory based on fuzzy complex random samples
Fuzzy Sets and Systems
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Intelligent data analysis and model interpretation with spectral analysis fuzzy symbolic modeling
International Journal of Approximate Reasoning
A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines
Information Sciences: an International Journal
Information Sciences: an International Journal
Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection
Expert Systems with Applications: An International Journal
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The relationship between support vector machines (SVMs) and Takagi-Sugeno-Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for every used kernel function. Restricted methods to extract rules from SVMs have been previously published. Their limitations are surpassed with the presented extraction method. The behavior of SVMs is explained by means of fuzzy logic and the interpretability of the system is improved by introducing the @l-fuzzy rule-based system (@l-FRBS). The @l-FRBS exactly approximates the SVM's decision boundary and its rules and membership functions are very simple, aggregating the antecedents with uninorms as compensation operators. The rules of the @l-FRBS are limited to two and the number of fuzzy propositions in each rule only depends on the cardinality of the set of support vectors. For that reason, the @l-FRBS overcomes the course of dimensionality and problems with high-dimensional data sets are easily solved with the @l-FRBS.