Integrating outlier filtering in large margin training

Journal of Zhejiang University: Science C

Volumn 12, Issue 5, 2011, Pages 362-370

  Zhou, Xi Chuan ^a   Shen, Hai Bin ^b   Ye, Jie Ping ^c  

^a CHONGQING UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c Arizona State University   (United States)

Author keywords

Multi stage relaxation; Outlier filter; Semi definite programming; Support vector machines

Indexed keywords

CLASSIFICATION TASKS; GLOBAL SOLUTIONS; ITERATIVE APPROACH; LARGE MARGIN CLASSIFIERS; MULTI-STAGE; OUTLIER FILTER; REAL WORLD DATA; SEMI-DEFINITE PROGRAMMING; SUPPORT VECTOR; LARGE MARGIN TRAININGS; MULTI STAGE;

OPTIMIZATION; SUPPORT VECTOR MACHINES; STATISTICS;

STATISTICS; SUPPORT VECTOR MACHINES;

EID: 79960104515     PISSN: 18691951     EISSN: 1869196X     Source Type: Journal    
DOI: 10.1631/jzus.C1000361     Document Type: Article

References (22)

1. Bousquet, O., Elisseeff, A., 2002. Stability and generalization. J.Mach. Learn. Res., 2(3):499-526. [doi:10.1162/153244302760200704] (Pubitemid 135712570)
2. Brodley, C.E., Friedl, M.A., 1996. Identifying and Eliminating Mislabeled Training Instances. Proc. 13th National Conf. on Artificial Intelligence, 1:799-805.
3. Cortes, C., Vapnik, V., 1995. Support vector networks. Mach. Learn., 20(3):273-297. [doi:10.1023/A:1022627411411]
4. Davy, M., Godsill, S., 2002. Detection of Abrupt Spectral Changes Using Support Vector Machines: an Application to Audio Signal Segmentation. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, p.1313-1316.
5. Eskin, E., Lee, W., Stolfo, S.J., 2001. Modeling System Calls for Intrusion Detection with Dynamic Window Sizes. Proc. DARPA Information Survivability Conf. and Exposition, p.1-11.
6. Fawcett, T., Provost, F.J., 1997. Adaptive fraud detection. Data Min. Knowl. Disc., 1(3):291-316. [doi:10.1023/A: 1009700419189] (Pubitemid 128000672)
7. Frank, A., Asuncion, A., 2010. UCI Machine Learning Repository. School of Information and Computer Science, University of California, Irvine.
8. Herbrich, R., Weston, J., 2000. Adaptive Margin Support Vector Machines for Classification. Advances in Large Margin Classifiers. MIT Press, Cambridge, Massachusetts, USA, p.281-295.
9. King, S.P., King, D.M., Astley, K., Tarassenko, L., Hayton, P., Utete, S., 2002. The Use of Novelty Detection Techniques for Monitoring High-Integrity Plant. Proc. Int. Conf. on Control Applications, 1:221-226. [doi:10.1109/CCA. 2002.1040189]
10. Krause, N., Singer, Y., 2004. Leveraging the Margin More Carefully. Proc. 21st Int. Conf. on Machine Learning, p.1-8. [doi:10.1145/1015330.1015344]
11. Laskov, P., Schafer, F., Kotenko, I., 2004. Intrusion Detection in Unlabeled Data with Quarter-Sphere Support Vector Machines. Proc. DIMVA, p.71-82.
12. Manevitz, L.M., Yousef, M., 2002. One-class SVMs for document classification. J. Mach. Learn. Res., 2(2):139-154.
13. Ratsch, G., Mika, S., Scholkopf, B., Muller, K.R., 2002. Constructing boosting algorithms from SVMs: an application to one-class classification. IEEE Trans. Pattern Anal. Mach. Intell., 24(9):1184-1199. [doi:10.1109/TPAMI.2002. 1033211] (Pubitemid 35242580)
14. Scholkopf, B., Smola, A.J., 2002. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge, Massachusetts, USA, p.135-141.
15. Song, Q., Hu, W., Xie, W., 2002. Robust support vector machine with bullet hole image classification. IEEE Trans. Syst. Man Cybern. C, 32(4):440-448.
16. Steinwart, I., Hush, D., Scovel, C., 2005. A classification framework for anomaly detection. J. Mach. Learn. Res., 6:211-232.
17. Tax, D., Ypma, A., Ypma, E., Duin, R.P.W., 1999. Support Vector Data Description Applied to Machine Vibration Analysis. Annual Conf. of the Advanced School for Computing and Imaging, p.398-405.
18. Tax, D.M.J., 2001. One-Class Classification: Concept-Learning in the Absence of Counter-Examples. PhD Thesis, Delft University of Technology, Delft, the Netherlands.
19. Thongkam, J., Xu, G., Zhang, Y., Huang, F., 2008. Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction. APWeb Workshop, p.99-109. [doi:10.1007/978-3-540-89376-9-10]
20. Wu, Y., Liu, Y., 2007. Robust truncated hinge loss support vector machines. J. Am. Statist. Assoc., 102(479):974-983. [doi:10.1198/ 016214507000000617]
21. Xu, L., Crammer, K., Schuurmans, D., 2006. Robust Support Vector Machine Training via Convex Outlier Ablation. Proc. National Conf. of Artificial Intelligence, 21:536-542.
22. Zhang, T., 2008. Multi-stage Convex Relaxation for Learning with Sparse Regularization. NIPS, p.1929-1936.