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Twin support vector machines are a powerful learning method for binary classification. Compared to standard support vector machines, they learn two hyperplanes rather than one as in standard support vector machines, and work faster and sometimes perform better than support vector machines. However, relatively little is known about their theoretical performance. As recent tightest bounds for practical applications, PAC-Bayes bounds are based on a prior and posterior over the distribution ofdoi:10.1109/ijcnn.2015.7280358 dblp:conf/ijcnn/XieS15 fatcat:a732alernvfldluz4lkg34u6ji