Abstract At present,the concept-drifting phenomena in various datasets receives considerable attention. Aiming at the classification of concept drift,an adaptive neighbor projection mean discrepancy support vector machine (NMD-SVM) is proposed. The concept of the neighbor projection mean discrepancy in the reproducing kernel Hilbert space is defined to measure the discrepancy between adjacent sub-classifiers,and the distribution characteristics of data are integrated into the process of global optimization. Thus,the adaptability of classification algorithm is enhanced. The experimental results on the simulation and real datasets validate the effectiveness of the proposed algorithm.
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2013, Vol. 26 
