中文摘要：

针对基于拉普拉斯支持向量机（LapSVM）的半监督分类方法需要将全部无标记样本加入训练样本集中训练得到分类器，算法需要的时间和空间复杂度高，不能有效处理大规模图像分类的问题，提出了模糊C-均值聚类（FCM）预选取样本的LapSVM图像分类方法。该方法利用FCM算法对无标记样本聚类，根据聚类结果选择可能在最优分类超平面附近的无标记样本点加入训练样本集，这些样本可能是支持向量，携带对分类有用的信息，其数量只是无标记样本的一少部分，因此使训练样本集减小。计算机仿真结果表明该方法充分利用了无标记样本所蕴含的判别信息，有效地提高了分类器的分类精度，降低了算法的时间和空间复杂度。

英文摘要：

In order to solve the problems that the semi-supervised classification method based on Laplacian Support Vector Machines （LapSVM） requires that all unlabeled sample should be added to the training set to train a classifier, the algorithm demands high time and space and cannot effectively deal with large-scale image classification, Fuzzy C-Mean （FCM） pre-selected sample of LapSVM image classification method was proposed. The method used FCM algorithm for clustering the unlabeled samples. According to the clustering results, unlabeled samples of near optimal separating hyper-plane were selected to add to the training sample set, and these samples may be support vector carrying useful information for classification. The quantity was only a small part of the unlabeled samples, so the training sample set was reduced. The simulation results show that this method takes advantage of the inherent discrimination information of the unlabeled samples, effectively improves the accuracy of classifiers, and reduces the algorithm＇s time and space complexity.