Classification method using human brain semi-supervised learning mechanism
人脑半监督学习机理分类法

Aimed at the problem that nearest neighbor method and k-nearest neighbor method can't obtain better classification effectiveness when there aren't enough labeled examples,a semi-supervised classification method is proposed in this paper.The method is based on the mechanism that unlabeled samples were used if human classify pattern involuntary.The method utilizes the nearest neighbor relationship between unlabeled samples to reduce the influence of the number of labeled samples on classification accuracy.The experimental results using the MNIST database of handwritten digits and the ORL face database show the method has higher classification accuracy than the nearest neighbor method and the k-nearest neighbor method if there aren't enough labeled samples.