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Machine Learning
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Neural Computation
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Least Squares Support Vector Machine Classifiers
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A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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Machine Learning
Efficient computations for large least square support vector machine classifiers
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Applied Intelligence
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Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The previous study of fuzzy support vector machines (FSVMs) provides a method to classify data with noises or outliers by manually associating each data point with a fuzzy membership that can reflect their relative degrees as meaningful data. In this paper, we introduce two factors in training data points, the confident factor and the trashy factor, and automatically generate fuzzy memberships of training data points from a heuristic strategy by using these two factors and a mapping function. We investigate and compare two strategies in the experiments and the results show that the generalization error of FSVMs is comparable to other methods on benchmark datasets. The proposed approach for automatic setting of fuzzy memberships makes the FSVMs more applicable in reducing the effects of noises or outliers.