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In the realm of machine learning, the data may contain additional attributes,known as privileged information (PI). The main purpose of PI is to assist inthe training of the model and then utilize the acquired knowledge to makepredictions for unseen samples. Support vector regression (SVR) is an effectiveregression model, however, it has a low learning speed due to solving a convexquadratic problem (QP) subject to a pair of constraints. In contrast, twinsupport vector regression (TSVR) is more efficient than SVR as it solves twoQPs each subject to one set of constraints. However, TSVR and its variants aretrained only on regular features and do not use privileged features fortraining. To fill this gap, we introduce a fusion of TSVR with learning usingprivileged information (LUPI) and propose a novel approach called twin supportvector regression with privileged information (TSVR+). The regularization termsin the proposed TSVR+ capture the essence of statistical learning theory andimplement the structural risk minimization principle. We use the successiveoverrelaxation (SOR) technique to solve the optimization problem of theproposed TSVR+, which enhances the training efficiency. As far as our knowledgeextends, the integration of the LUPI concept into twin variants of regressionmodels is a novel advancement. The numerical experiments conducted on UCI,stock and time series data collectively demonstrate the superiority of theproposed model.

TSVR+: Twin support vector regression with privileged information