References
- Belkin, M., Niyogi, P. and Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from laveled and unlabeled examples. Journal of Machine Learning Research, 1, 1-48.
- Chapelle, O., Scholkopf, B. and Zien, A. (2006). Semi-supervised learning, MIT Press, Cambridge, MA.
- Cortes, C. and Mohri, M. (2007). On transductive regression. In Advances in Neural Information Processing System, 19, 305-312.
- Exterkate, P. (2012). Model selection in kernel ridge regression, CREATES Research Papers from School of Economics and Management, University of Aarhus, Aarhus, Denmark.
- Hofmann, T., Scholkopf, B. and Smola, A. J. (2008) Kernel methods in machine learning. Annals of Statistics, 36, 1171-1220. https://doi.org/10.1214/009053607000000677
- Kimeldorf, G. S. and Wahba, G. (1971). Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis and Applications, 33, 82-95. https://doi.org/10.1016/0022-247X(71)90184-3
- Lafferty, J. and Wasserman, L. (2008). Statistical analysis of semi-supervised regression. In Advances in Neural Information Processing Systems, 20, 801-808.
- Lin, H. and Lin, C. (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods, Technical Report, Department of Computer Science, National Taiwan University, Taipei, Taiwan.
- Mercer, J. (1909). Functions of positive and negative type, and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London A, 209, 415-446. https://doi.org/10.1098/rsta.1909.0016
- Nigam, K. and Ghani, R. (2000). Analyzing the effectiveness and applicability of co-training. Ninth International Conference on Information and Knowledge Management, 86-93.
- Niyogi, P. (2008). Manifold regularization and semi-supervised learning: Some theoretical analyses, Technical Report TR-2008-01, Computer Science Department, University of Chicago, Chicago, IL.
- Rosenberg, C., Hebert, M. and Schneiderman, H. (2005). Semi-supervised self-training of object detection models. Seventh IEEE Workshop on Applications of Computer Vision, 1, 29-36.
- Rosipal, R. and Trejo, L. (2001) Kernel partial least squares regression in reproducing kernel Hilbert space. Journal of Machine Learning Research, 2, 1667-1689.
- Seok, K. (2012). Study on semi-supervised local constant regression estimation. Journal of the Korean Data & Information Science Society, 23, 579-585. https://doi.org/10.7465/jkdi.2012.23.3.579
- Singh, A., Nowak, R. and Zhu, X. (2008). Unlabeled data: Now it helps, now it doesn't. In Advances in Neural Information Processing Systems, 21, 1513-1520.
- Suykens, J.A.K., Gastel, T. V., Bravanter, J. D., Moore, B. D. and Vandewalle, J. (2002). Least squares support vector machines, World Scientific, London.
- Szummer, M. and Jaakkola, T. (2002). Information regularization with partially labeled data. In Advances in Neural Information Processing Systems, 15, 1025-1032.
- Ungar, L. H. (1995). UPenn ChemData repository [Machine-readable data repository], Philadelphia, PA. Available electronically via ftp://ftp.cis.upenn.edu/pub/ungar/chemdata.
- Vapnik, V. (1998). Statistical learning theory, Wiley, New York.
- Wang, M., Hua, X., Song, Y., Dai, L. and Zhang, H. (2006). Semi-supervised kernel regression. In Proceeding of the Sixth IEEE International Conference on Data Mining, 1130-1135.
- Xu, S., An. X., Qiao, X., Zhu, L. and Li, L. (2011) Semisupervised least squares support vector regression machines. Journal of Information & Computational Science, 8, 885-892.
- Xu, Z., King, I. and Lyu, M. R. (2010). More than semi-supervised learning, LAP LAMBERT Academic Publishing, London.
- Zhou, X., Ghahramani, Z. and Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning, 912-919.
- Zhou, Y. and Goldman, S. (2004). Democratic co-learing. In Proceedings of the16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI2004), 594-602.
- Zhou, Z. and Li, M. (2007). Semi-supervised regression with co-training style algorithm. IEEE Transactions on Knowledge and Data Engineering, 19, 1479-1493. https://doi.org/10.1109/TKDE.2007.190644
- Zhu, D. (2005). Semi-supervised learning literature survey, Technical Report, Computer Sciences Department, University of Wisconsin, Madison, WI.
- Zhu, X. and Goldberg, A. (2009). Introduction to semi-supervised learning, Morgan & Claypool, London.
Cited by
- Smoothing parameter selection in semi-supervised learning vol.27, pp.4, 2016, https://doi.org/10.7465/jkdi.2016.27.4.993
- Semisupervised support vector quantile regression vol.26, pp.2, 2015, https://doi.org/10.7465/jkdi.2015.26.2.517
- A transductive least squares support vector machine with the difference convex algorithm vol.25, pp.2, 2014, https://doi.org/10.7465/jkdi.2014.25.2.455
- Semi-supervised regression based on support vector machine vol.25, pp.2, 2014, https://doi.org/10.7465/jkdi.2014.25.2.447