Branch and Bound for Semi-Supervised Support Vector Machines

NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Volumn , Issue , 2006, Pages 217-224

  Chapelle, Olivier ^a   Sindhwani, Vikas ^b   Keerthi, S Sathiya ^c  

^a Max Planck Institut fÃijr extraterrestrische Physik   (Germany)

^b UNIVERSITY OF CHICAGO   (United States)

^c YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OPTIMAL SYSTEMS;

'CURRENT; BRANCH AND BOUND TECHNIQUES; BRANCH AND BOUNDS; LEARN+; LOCAL MINIMUM PROBLEM; LOWER DENSITY; OPTIMAL SOLUTIONS; OPTIMIZATION PROBLEMS; SEMI-SUPERVISED; SUPPORT VECTORS MACHINE;

SUPPORT VECTOR MACHINES;

EID: 85158026936     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. K. Bennett and A. Demiriz. Semi-supervised support vector machines. In Advances in Neural Information processing systems 12, 1998.
2. G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In Advances in Neural Information Processing Systems, pages 409-415, 2000.
3. O. Chapelle, M. Chi, and A. Zien. A continuation method for semi-supervised svms. In International Conference on Machine Learning, 2006.
4. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, 2006. in press. www.kyb.tuebingen.mpg.de/ssl-book/.
5. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46:131-159, 2002.
6. O. Chapelle and A. Zien. Semi-supervised classification by low density separation. In Tenth International Workshop on Artificial Intelligence and Statistics, 2005.
7. R. Collobert, F. Sinz, J. Weston, and L. Bottou. Large scale transductive SVMs. Journal of Machine Learning Research, 7:1687-1712, 2006.
8. W. Gander, G. H. Golub, and U. Von Matt. A constrained eigenvalue problem. Linear Algebra and its Applications, 114/115:815-839, 1989.
9. T. Joachims. Transductive inference for text classification using support vector machines. In International Conference on Machine Learning, 1999.
10. P.M. Pardalos and G.P. Rodgers. Computational aspects of a branch and bound algorithm for quadratic zero-one programming. Computing, 45:131-144, 1990.
11. M. Seeger. A taxonomy of semi-supervised learning methods. In O. Chapelle, B. Schölkopf, and A. Zien, editors, Semi-Supervised Lerning. MIT Press, 2006.
12. V. Sindhwani, S. Keerthi, and O. Chapelle. Deterministic annealing for semi-supervised kernel machines. In International Conference on Machine Learning, 2006.
13. V. Sindhwani, P. Niyogi, and M. Belkin. Beyond the point cloud: From transductive to semi-supervised learning. In International Conference on Machine Learning, 2005.
14. V. Vapnik and A. Sterin. On structural risk minimization or overall risk in a problem of pattern recognition. Automation and Remote Control, 10(3):1495-1503, 1977.
15. V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, Inc., New York, 1998.
16. W. Wapnik and A. Tscherwonenkis. Theorie der Zeichenerkennung. Akademie Verlag, Berlin, 1979.
17. X. Zhu and Z. Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report 02-107, CMU-CALD, 2002.