Deterministic annealing for semi-supervised kernel machines

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 841-848

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

^a THE UNIVERSITY OF CHICAGO   (United States)

^b YAHOO RESEARCH   (United States)

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; OPTIMIZATION; PROBLEM SOLVING; SUPPORT VECTOR MACHINES;

DATASETS; GLOBAL OPTIMIZATION FRAMEWORK; KERNEL MACHINES; LABEL-SWITCHING-RETRAINING PROCEDURE;

GENETIC ALGORITHMS;

EID: 34250761401     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143950     Document Type: Conference Paper

References (17)

1. Bennett, K. & Demirez, A. (1999). Semi-supervised Support Vector Machines NIPS 13.
2. Bilbro, G., Snyder, W. E & Mann, R. (1991). Mean-field approximation minimizes relative entropy, Journal of Optical Society of America A, vol. 8, No 2.
3. Blake, A. & Zisserman, A. (1987). Visual Reconstruction, MIT Press.
4. Collobert, R., Weston, J. & Bottou, L. (2005). Trading Convexity for Scalability, NIPS 19.
5. Chapelle, O. (2006). Training a Support Vector Machine in the Primal, Tech. report, MPI, Tuebingen.
6. Chapelle, O., Chi, M. & Zien, A. (2006). A Continuation Method for Semi-Supervised SVMs, submitted to ICML 2006.
7. Chapelle, O. & Zien, A. (2005). Semi-Supervised Classification by Low Density Separation, AI & Statistics.
8. Dunlavy, D. M & O'Leary, D. P. (2005). Homotopy Optimization methods for Global Optimization, Tech. Report, CS-TR-4773, Univ. of Maryland.
9. Gartner, T., Le, Q. V., Burton, S., Smola, A. J., & Vishwanathan, V. (2005). Large Scale Multiclass Transduction, NIPS 19.
10. Hofmann, T. & Buhmann, J. M. (1997). Pairwise Data Clustering by Deterministic Annealing, IEEE TPAMI, No. 1, pp. 1-14.
11. Joachims T. (1999). Transductive Inference for Text Classification using Support Vector Machines, ICML.
12. Keerthi, S. S., & DeCoste, D. (2005) A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs, JMLR, vol. 6, pp. 341-361
13. Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1983). Optimization by Simulated annealing, Science, vol. 220, pp. 671-680.
14. Nocedal, J. & Wright, S. J. (2000). Numerical Optimzation, Springer.
15. Rose, K. (1998). Deterministic annealing for clustering, compression, classification, regression, and related optimization problems, Proc. of IEEE, vol. 80, no. 11, pp. 2210-2239
16. Sindhwani, V. & Keerthi, S. S. (2006). Large Scale Linear Semi-supervised SVMs, ACM SIGIR.
17. Vapnik, V. & Sterin, A. (1977). On structural risk minimization or overall risk in a problem of pattern recognition, Automation and Remote Control, vol. 10(3), pp. 1495-1503.