Reference

This package is built based on the following works:

Works led by Dr. Li.

  1. Yu-Feng Li, De-Ming Liang. Safe Semi-Supervised Learning: A Brief Introduction. Frontiers of Computer Science (FCS). in press.
  2. De-Ming Liang, Yu-Feng Li. Lightweight label propagation for large-scale network data. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI‘18), Stockholm, Sweden, 2018, pp.3421-3427.
  3. Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou. Learning safe prediction for semi-supervised regression. In: Proceedings of the 31st AAAI conference on Artificial Intelligence (AAAI‘17), San Francisco, CA, 2017, pp.2217-2223.
  4. Yu-Feng Li, Shao-Bo Wang, Zhi-Hua Zhou. Graph quality judgement: A large margin expedition. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI‘16), New York, NY, 2016, pp.1725-1731.
  5. Yu-Feng Li, James Kwok and Zhi-Hua Zhou. Towards safe semi-supervised learning for multivariate performance measures. In: Proceedings of the 30th AAAI conference on Artificial Intelligence (AAAI‘16), Phoenix, AZ, 2016, pp. 1816-1822.
  6. Yu-Feng Li and Zhi-Hua Zhou. Towards making unlabeled data never hurt. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 37(1):175-188, 2015.
  7. Yu-Feng Li, Ivor Tsang, James Kwok and Zhi-Hua Zhou. Convex and Scalable Weakly Labeled SVMs. Journal of Machine Learning Research (JMLR), 14:2151-2188, 2013.
  8. Yu-Feng Li and Zhi-Hua Zhou. Towards making unlabeled data never hurt. In: Proceedings of the 28th International Conference on Machine Learning (ICML‘11), Bellevue, WA, 2011, pp.1081-1088.

Semi-supervised Learning

  1. Blum, Avrim, and Tom Mitchell. Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT‘98), New York, NY, 1998, pp.92-100.
  2. Joachims, Thorsten. Transductive Inference for Text Classi cation using Support Vector Machines. In: Proceedings of the 16th International Conference on Machine Learning (ICML‘99), San Francisco, CA, 1999. pp.200-209.
  3. Zhu, Xiaojin, Zoubin Ghahramani, and John D. Lafferty. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. In: Proceedings of the 20th International conference on Machine learning (ICML‘03), Washington, DC, 2003, pp. 912-919.

Third-party Library

  1. Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel et al. Scikit-learn: Machine learning in Python. Journal of machine learning research (JMLR), 12: 2825-2830, 2013.
  2. C.-C. Chang and C.-J. Lin. LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011..
  3. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification . Journal of Machine Learning Research (JMLR), 9: 1871-1874, 2008..