中文摘要：

针对具有多观测样本的相似不完整数据分类问题,提出基于SVM和多观测样本的相似数据分类算法.每类数据的多观测样本集由属于同一模式的单观测样本组成,每次分类时,对两个多观测样本集的标签做两次假设,通过比较不同标签假设下的分类误差确定多观测样本集的标签.该方法同时充分利用了样本类内的相关性和类间的差异性,实现了相似不完整数据的分类.实验结果验证了所提出方法的有效性.

英文摘要：

A classification method based on SVM is proposed for classification tasks of similar and incomplete multi-observation data. All single observation samples in a multi-observation set belong to a same class. In each classification, different assumptions about the class of multi-observation sets are made, two classification errors are obtained respectively for each assumption, and the final label is determined by comparing two classification errors. The proposed method takes advantage of the correlation within the same classes and the difference between different classes, and achieves the effective classification for similar and incomplete multi-observation data. Experimental results show the effectiveness of the proposed method.