The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Towards Simple, Easy-to-Understand, yet Accurate Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Improved parameter tuning algorithms for fuzzy classifiers
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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In this paper, we discuss fuzzy classifiers based on Kernel Discriminant Analysis (KDA) for two-class problems. In our method, first we employ KDA to the given training data and calculate the component that maximally separates two classes in the feature space. Then, in the one-dimensional space obtained by KDA, we generate fuzzy rules with one-dimensional membership functions and tune the slopes and bias terms based on the same training algorithm as that of linear SVMs. Through the computer experiments for two-class problems, we show that the performance of the proposed classifier is comparable to that of SVMs, and we can easily and visually analyze its behavior using the degrees of membership functions.