  Semi-supervised learning, which has emerged from the beginning of this
century, is a new type of learning method between traditional supervised
learning and unsupervised learning. The main idea of semi-supervised learning
is to introduce unlabeled samples into the model training process to avoid
performance (or model) degeneration due to insufficiency of labeled samples.
Semi-supervised learning has been applied successfully in many fields. This
paper reviews the development process and main theories of semi-supervised
learning, as well as its recent advances and importance in solving real-world
problems demonstrated by typical application examples.
