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Support vector regression (SVR), which has been successfully applied to a variety of real-world problems, simultaneously minimizes the regularization error and empirical risk with a suitable penalty factor. However, it does not embed any prior information of data into the learning process. In this paper, by introducing a new term to seek a projection axis of data points, we present a novel projection...
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