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Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. However, an inherent shortage of twin support vector machines is that the resultant hyperplanes are very sensitive to outliers in data. In this paper, we propose centroid twin...
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