In supervised learning methods, a large amount of labeled data is necessary to find reliable classification boundaries to train a classifier. However, it is hard to obtain a large amount of labeled data in practice and it is time-consuming with a lot of cost to obtain labels of data. Although unlabeled data is comparatively plentiful than labeled data, most of supervised learning methods are not designed to exploit unlabeled data. Self-training is one of the semisupervised learning methods that alternatively repeat training a base classifier and labeling unlabeled data in training set. Most self-training methods have adopted confidence measures to select confidently labeled examples because high-confidence usually implies low error. A major difficulty of self-training is the error amplification. If a classifier misclassifies some examples and the misclassified examples are included in the labeled training set, the next classifier may learn improper classification boundaries and generate more misclassified examples. Since base classifiers are built with small labeled dataset and are hard to earn good generalization performance due to the small labeled dataset. Although improving training procedure and the performance of classifiers, error occurrence is inevitable, so corrections of self-labeled data are necessary to avoid error amplification in the following classifiers. In this paper, we propose a deep neural network based approach for alleviating the problems of self-training by combining schemes: pre-training, dropout and error forgetting. By applying combinations of these schemes to various dataset, a trained classifier using our approach shows improved performance than trained classifier using common self-training.

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