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中文摘要：
针对加权kNN（k-Nearest Neighbor）方法在对样本进行分类时，仅仅只利用了它的k近邻点来进行分类决策的不足，提出了一种序列的加权kNN分类方法．该方法在对某个测试样本进行分类时，除了利用它k近邻点所提供的类别信息外，还有效地利用了前面已分类样本的类别信息，这使得测试样本的分类决策更加合理和有效．在Cohn-Kanade人脸库上进行的表情识别实验表明，在序列样本分类的场合，该方法的分类效果比加权kNN方法更好．
英文摘要：
Aim at the defect that weighted k-nearest neighbor method classifies one test sample only using the class information of its k-nearest samples, a sequential weighted k-nearest neighbor classification method is proposed in this paper. Not only the class information offered by k-nearest neighbor points of test sample but also the class information of previous test sample is used for classification in the proposed method. So its decision-making processing is more reasonable and effective. The experimental results of facial expression recognition in Cohn-Kanade face database show the method is better than weighted k-nearest neighbor method for the classification of sequential samples.
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