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Over the past decades, regularization theory is widely applied in various areas of machine learning to derive a large family of novel algorithms. Traditionally, regularization focuses on smoothing only, and does not fully utilize the underlying discriminative knowledge which is vital for classification. In this paper, we propose a novel regularization algorithm in the least-squares sense, called Discriminatively Regularized Least-Squares Classification (DRLSC) method, which is specificallydoi:10.1016/j.patcog.2008.07.010 fatcat:2ikbssa3mvhqdmhi7cg52ebg2a