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Efficient and interpretable fuzzy classifiers from data with support vector learning
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Fuzzy one-class support vector machines
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T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
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Fuzzy Sets and Systems
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ICS'08 Proceedings of the 12th WSEAS international conference on Systems
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
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Engineering Applications of Artificial Intelligence
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
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Expert Systems with Applications: An International Journal
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Fuzzy Sets and Systems
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CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
Information Sciences: an International Journal
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IEEE Transactions on Fuzzy Systems
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Information Sciences: an International Journal
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IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Fuzzy Systems
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Mining efficient and interpretable fuzzy classifiers from data with support vector learning
ICAI'05/MCBC'05/AMTA'05/MCBE'05 Proceedings of the 6th WSEAS international conference on Automation & information, and 6th WSEAS international conference on mathematics and computers in biology and chemistry, and 6th WSEAS international conference on acoustics and music: theory and applications, and 6th WSEAS international conference on Mathematics and computers in business and economics
T-S fuzzy modeling based on support vector learning
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Smooth support vector learning for fuzzy rule-based classification systems
Intelligent Data Analysis
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TS-fuzzy modeling based on ε-insensitive smooth support vector regression
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper describes a fuzzy modeling framework based on support vector machine, a rule-based framework that explicitly characterizes the representation in fuzzy inference procedure. The support vector learning mechanism provides an architecture to extract support vectors for generating fuzzy IF-THEN rules from the training data set, and a method to describe the fuzzy system in terms of kernel functions. Thus, it has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions. The performance of the proposed approach is compared to other fuzzy rule-based modeling methods using four data sets.