References
- Asuncion, A. and D. J. Newman, UCI Machine Learning Repository (http://www. ics.uci.edu/: mlearn/MLRepository.html), Irvine, CA: University of California, School of Information and Computer Science, 2007.
- Bennett, K. P. and A. Demiriz, "Semi-supervised support vector machines," Advance in Neural Information Processing Systems(NIPS) 10 (1998), MIT Press.
- Bie, T. a nd De, N. Christianini, "Convex methods for transduction," Advances in Neural Information Processing Systems(NIPS) 16 (2004), MIT Press.
- Blum, A. and T. Mitchell, "Combining labeled and unlabeled data with co-training," COLT: Proceedings of the Workshop on Computational Learning Theory 1998.
- Blum, A. and S. Chawla, "Learning from labeled and unlabeled data using graph mincuts," Proceedings of the 18th International Conference on Machine Learning(ICML) ACM Press, 2001.
- Chang, C-C. and C-J. Lin, UBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/cjlin/libsvm. 2001.
- Chapelle, O., J. Weston, and B. Scholkopf, "Cluster kernels for semi-supervised learning," Advances in Neural Information Processing Systems (NIPS) 15 (2003), MIT Press.
- Cristianini, J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, 2000.
- Culp, M. and G. Michailidis, "An iterative algorithm for extending learners to a semi supervised setting," The 2007 Joint Statistical Meeting(JSM) 2007.
- Hull, J. J., "A database for handwritten text recognition research," IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (1994), 550-554. https://doi.org/10.1109/34.291440
- Joachims, T., "Transductive inference for text classification using support vector machines," Proceedings of the 20th International Conference on Machine Learning(ICML) ACM Press, 1999.
- Joachims, T., "Transductive learning via spectral graph partitioning," Proceedings of the 20th International Conference on Machine Learning(ICML), ACM Press, 2003.
- Olivier, C., V. Sindhwani, and S. S. Keerthi, "Branch and bound for semisupervised support vector machines," Advances in Neural Information Processing Systems(NIPS) 18 (2006), MIT Press.
- Park, C-K., "A branch-and-bound algorithm for finding an optimal solution of transductive support vector machines," Journal of the Korean Operations Research and Management Science Society 31,2 (2006), 69-85.
- Platt, J., "Fast training of support vector machines using sequential minimal optimization," In: B. Scholkopf, C. J. C. Burges, and A. J. Smola, eds., Advances in Kernel Methods-Support Vector Learning, MIT Press, (1999), 85-208.
- Ratsaby, J. and S. Venkatesh, "Learning from a mixture of labeled and unlabeled examples with parametric side information," Proceedings of the Eighth Annual Conference on Computational Learning Theory (1995), 412-417.
- Scholkopf, B. and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2002.
- Shi, J. and J. Malik, "Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000), 888-905. https://doi.org/10.1109/34.868688
- Szummer, M., T. Jaakkola, "Partially labeled classification with Markov random walks," Advances in Neural Information Processing Systems(NIPS) 14 (2002), MIT Press.
- Vapnik, V., Statistical Learning Theory, Wiley, 1998.
- Virginia, R., "Learning classification with unlabeled data," Advances in Neural Information Processing Systems(NIPS) 5 (1993), MIT Press.
- Yu, S. X. and J. Shi, "Grouping with bias," Advances in Neural Information Processing Systems(NIPS) 14 (2001), MIT Press
- Zhu, X., Z. Ghahramani, and J. Lafferty, "Semi-supervised learning using Gaussian fields and harmonic functions," Proceedings of the 20th International Conference on Machine Learning (ICML), ACM Press, 2003.
- Zhu, X. and A. B. Goldberg, Introduction to Semi-Supervised Learning, Morgan and Claypool Publishers, 2009.