Cost-sensitive universum-SVM

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 1, Issue , 2012, Pages 220-225

  Dhar, Sauptik ^a   Cherkassky, Vladimir ^a  

^a University of Minnesota   (United States)

Author keywords

Cost sensitive SVM; learning through contradiction; support vector machines; Universum SVM

Indexed keywords

CLASSIFICATION METHODS; COST-SENSITIVE; COST-SENSITIVE SVM; EMPIRICAL - COMPARISONS; HIGH DIMENSIONAL DATA; INPUT FEATURES; LEARNING SETTINGS; LEARNING THROUGH CONTRADICTION; MISCLASSIFICATION COSTS; NUMBER OF DATUM; UNIVERSUM SVM;

COSTS;

SUPPORT VECTOR MACHINES;

EID: 84873596372     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.45     Document Type: Conference Paper

References (21)

1. V. N. Vapnik, Estimation of Dependencies Based on Empirical Data:Empirical Inference Science: Afterword of 2006. New York:Springer-Verlag, 2006.
2. J. Weston,R. Collobert,F. Sinz,L. Bottou, and V. Vapnik, "Inferencewith the Universum," Proc. ICML,2006, pp. 1009-1016.
3. V. Cherkassky, S. Dhar, and W. Dai, "Practical Conditions forEffectiveness of the Universum Learning,", IEEE Transactions onNeural Networks,vol.22, no. 8, pp. 1241-1255, Aug 2011.
4. V. Cherkassky, and W. Dai, "Empirical Study of the Universum SVMLearning for High-Dimensional Data," in Proc. ICANN, 2009.
5. F. Sinz, O. Chapelle, A. Agarwal, and B. Schölkopf, "An analysis ofinference with the Universum," In Proc. of 21st Annual Conferenceon Neural Information Processing Systems, 2008, pp. 1-8.
6. T. T. Gao, Z.X Yang, L. Jing, "On Universum-Support VectorMachines",The Eighth International Symposium on OperationsResearch and Its Applications, China, 2009, pp. 473-480.
7. D. Zhang, J. Wang, F. Wang, and C. Zhang, "Semi- supervisedclassification with Universum," Proceedings of the 8th SIAMConference on Data Mining (SDM), 2008, pp. 323-333.
8. S. Chen and C. Zhang, "Selecting informative Universum sample forsemi-supervised learning," in Proc. Int. Joint Conf. Artif. Intell.,2009, pp. 1016-1021.
9. X. Bai and V. Cherkassky, "Gender classification of human facesusing inference through contradictions," in Proc. Int. Joint Conf.Neural Netw., Hong Kong, Jun. 2008, pp. 746-750.
10. C. Shen, P. Wang, F. Shen, H. Wang, "UBoost: Boosting with theUniversum", IEEE Transaction on Pattern Analysis and MachineIntelligence, 2011.
11. P.N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining.New York: Pearson Education, 2006.
12. G. M. Weiss, K. McCarthy, B. Zabar,"Cost-Sensitive Learning vs.Sampling: Which is Best for Handling Unbalanced Classes withUnequal Error Costs?", DMIN 2007,pp. 35-41.
13. C. Elkan, "The foundations of cost-sensitive learning", Proceedingsof the Seventeenth International Joint Conference on ArtificialIntelligence, 2001.
14. V. Cherkassky and F. Mulier, Learning from Data Concepts: Theoryand Methods, 2nd ed. New York: Wiley, 2007.
15. Y. Lin, Y. Lee, and G. Wahba, "Support vector machines forclassification in nonstandard situations", Machine Learning, vol.46,pp. 191-202, 2002.
16. UniverSVM. [WWW page].URL: http://mloss.org/software/view/19/.
17. Cost Sensitive Univerum Software. [WWW page]. URL:http://www.ece.umn.edu/ users/cherkass/predictive-learning/SOFTWARES.html.
18. V. Cherkassky and S. Dhar, "Simple method for interpretation of highdimensional nonlinear SVM classification models," in Proc. Int.Conf. Data Min., Las Vegas, NV, Jul. 2010, pp. 267-272.
19. M. Fanty, R. Cole. "Spoken letter recognition", Advances in NeuralInformation Processing Systems 3. San Mateo, CA: MorganKaufmann, 1991.
20. S. Roweis, sam roweis: data. [WWW page]. URLhttp://www.cs.nyu.edu/ ~roweis/data.html.
21. The German Traffic Sign Recognition Benchmark. [WWW page].URL http://benchmark.ini.rub.de/?section=gtsrb&subsection= dataset#resultanalysis.