针对NN(nearest neighbor)和kNN(k-nearest neighbor)方法在标记样本较少时,分类正确率不高的缺陷,根据人脑分类样本时,自觉地利用未标记样本的半监督学习机理,提出一种人脑半监督学习机理分类方法。该方法利用未标记样本间的近邻关系,减少了标记样本数量对分类正确率的影响程度。在MNIST手写体数字库和ORL人脸库上的样本分类实验表明,在标记样本数较少的情况下,该方法的分类正确率比NN和kNN方法高得多。

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.