A Review of Semi-Supervised Learning Theories and Recent Advances

作者：Tu, Enmei; Yang, Jie^*

来源：Journal of Shanghai Jiaotong University, 2018, 52(10): 1280-1291.

DOI：10.16183/j.cnki.jsjtu.2018.10.017

• Development process
• Graph Laplacian
• Learning methods
• Model training
• Real-world problem
• Semi
• supervised learning
• Typical application
• Unlabeled samples

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 model training process to avoid performance (or model) degeneration due to insufficiency of labeled samples. Semi-supervised learning has been applied sucessfully 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.

• 出版日期2018
• 单位上海交通大学