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We propose a regularized extension to supervised maximum figure-of-merit learning to improve its generalization capability and successfully extend it to semi-supervised learning. The proposed method can be used to approximate any objective function consisting of the commonly used performance metrics. We first derive detailed learning algorithms for supervised learning problems and then extend it to more general semi-supervised scenarios, where only a small part of the training data is labeled. The effectiveness of the proposed approach is justified by several text categorization experiments on different datasets. The novelty of this paper lies in several aspects: 1) Tikhonov regularization is used to alleviate potential overfitting of the maximum figure-of-merit criteria; 2) the regularized maximum figure-of-merit algorithm is successfully extended to semi-supervised learning tasks; 3) the proposed approach has good scalability to large-scale applications.