The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
AI Game Programming Wisdom
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
Linear discriminant analysis for interval data
Computational Statistics
Introduction to Interval Analysis
Introduction to Interval Analysis
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Clustering: A neural network approach
Neural Networks
Unsupervised pattern recognition models for mixed feature-type symbolic data
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.