1 |
Adankon, M., Cheriet, M. and Biem, A. (2009). Semisupervised least squares support vector machine. IEEE Tranactions on Neural Networks, 20, 1858-1870.
DOI
ScienceOn
|
2 |
An, L. and Tao, P. (1997). Solving a class of linearly constrained indefinite quadratic problems by D.C. algorithms. Journal of Global Optimization, 11, 253-285.
DOI
ScienceOn
|
3 |
Astotino, A. and Fuduli, A. (2005). Nonsmooth optimization technique for semisupervised classificatin. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 2135-2142.
|
4 |
Blum, A. and Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. Proceedings of the 11th Annual Conference on Computational Learning Theory, Madison, Wisconsin, 92-100.
|
5 |
Blum, A. and Chawla, S. (2001). Learning from labeled and unlabeled data using graph minicuts. In Proceedings of ICML 2001, 19-26.
|
6 |
Chapelle, O. and Zien, A. (2005). Semi-supervised classification by low density separarion. In Proceedings of tht 10th International Workshop on Artificial Intelligence and Statistics, 57-64.
|
7 |
Chapelle, O., Sindhwani, V. and Keerthi, S. (2008). Optimization techniques for semisupervised support vector machines. Journal of Machine Learning Research, 9, 203-233.
|
8 |
Chen, Y., Wang, G. and Dong, S. (2002). Learning with progressive transductive support vector machine. Proceedings of International Conference on Data Mining, Maebashi City, Japan, 67-74.
|
9 |
Craven, P. and Wahba, G. (1979). Smothing noisy data with spline functions. Numerical Mathematics, 31, 377-403.
|
10 |
Seok, K. (2013). A study on semi-supervised kernel ridge regression estimation. Journal of the Korean Data & Information Science Society, 24, 341-353.
과학기술학회마을
DOI
ScienceOn
|
11 |
Joachims, T. (1999). Transductive inference for text classification using support vecter machines. Proceedings of International conference on Machine Learning, Bled, Slovenia, 200-209.
|
12 |
Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society A, 415-446.
|
13 |
Seok, K. (2012). Study on semi-supervised local constant regression estimation. Journal of the Korean Data & Information Science Society, 23, 579-585.
과학기술학회마을
DOI
ScienceOn
|
14 |
Suykens, J. A. K. (2000). Least squares support vector machine for classication and nonlinear modeling. Neural Network World, Special issue on PASE 2000, 10, 29-48.
|
15 |
Suykens, J. A. K. and Vanderwalle, J. (1999). Least square support vector machine classifier, Neural Processing Letters, 9, 293-300.
DOI
ScienceOn
|
16 |
Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B. and Vanthienen, J. (2002). Least squares support vector machines, World Scientific, Singapore.
|
17 |
Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
|
18 |
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.
|
19 |
Vapnik, V. N. (1998). Statistical learning theory, John Wiley, New York.
|
20 |
Wang, J., Shen, X. and Pan, W. (2007). On transductive support vector machine. Contemporary Mathematics, 43, 7-19.
|
21 |
Xu, Z., King, I. and Lyu, M. R. (2010). More than semi-supervised learning, LAP LAMBERT Academic Publishing, Germany.
|
22 |
Zhang, R., Wang, W., Ma, Y. and Men, C. (2009). Least square transduction support vector machine. Neural Processing Letters, 29, 133-142.
DOI
|
23 |
Zhu, X. and Goldberg, A. (2009). Introduction to semi-supervised learning, Morgan & Claypool Publishers, CA.
|