Abstract: Convolutional neural network (CNN) is a kind of important deep neural network.However,amounts of labeled samples are needed in the training process of CNN,which largely limits its applications.For the problem,the co-training process of CNN was analyzed,and a co-training algorithm CAMC based on the iterative evolution of classifiers was given.The advantages of CNN and co-training of multiple classifiers are combined in CAMC.Firstly,multiple features are drawn using different convolutional kernels to build up different CNN classifiers.Then to continually improve the classification performance,some labeled samples together with many unlabeled samples are employed to co-train the multi classifiers.The experimental results based on the standard data sets of human expression demonstrates that,compared with traditional expression recognition methods LBP and Gabor,the performance of CAMC can be continually improved,thus it has higher classification accuracy.

Key words: Machine learning,Convolutional neural network,Co-training,Image recognition,Multiple classifiers