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In many cases, the output of a classifier should be a calibrated posterior probability to enable post-processing. However, twin support vector machines (TWSVM) do not provide such probabilities. In this paper, we propose a TWSVM probability model, called PTWSVM, to estimate the posterior probability. Note that our model is quite different from the SVM probability model because of the different mechanism...
In this paper we formulate a least squares version of the recently proposed projection twin support vector machine (PTSVM) for binary classification. This formulation leads to extremely simple and fast algorithm, called least squares projection twin support vector machine (LSPTSVM) for generating binary classifiers. Different from PTSVM, we add a regularization term, ensuring the optimization problems...
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