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中文摘要：
数据的概念漂移特性是广泛存在的．针对渐变概念漂移的分类问题，提出一种自适应近邻投影均值差支持向量机算法．该算法基于结构风险最小化模型，以再生核Hilbert空间中近邻投影均值差为相邻分类器间差异的度量．在全局优化中融人数据自身的分布特征．提高算法的适应性．在模拟数据和真实数据集上的实验结果表明该算法是有效的．
英文摘要：
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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