Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Fuzzy classifiers based on kernel discriminant analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
An adaptive neural fuzzy filter and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy classifier with ellipsoidal regions
IEEE Transactions on Fuzzy Systems
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We propose two methods for tuning membership functions of a fuzzy classifier by the support-vector-machine (SVM) like training. For each class, we define a membership function in the feature space. In the first method, we tune the slopes of the membership functions so that the margin between classes is maximized. This method is similar to a linear all-at-once SVM. We call this AAO tuning. In the second method, for each class the membership function is tuned so that the margin between the class and the remaining classes are maximized. This method is similar to a linear one-against-all SVM. This is called OAA tuning. According to the computer experiment, the kernel-discriminant-analysis (KDA) based fuzzy classifiers tuned by AAO tuning and by OAA tuning and SVM show comparable classification performance.