Label-noise reduction with support vector machines

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2012, Pages 3504-3508

  Fefilatyev, Sergiy ^a   Shreve, Matthew ^a   Kramer, Kurt ^a   Hall, Lawrence ^a   Goldgof, Dmitry ^a   Kasturi, Rangachar ^a   Daly, Kendra ^a   Remsen, Andrew ^a   Bunke, Horst ^b  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

^b UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN EXPERT; LARGE DATASET; LARGE DATASETS; NOISY LABELS; RANDOM NOISE; SUPPORT VECTOR; SVM CLASSIFIERS;

CHARACTER RECOGNITION; EXPERIMENTS; NOISE ABATEMENT;

SUPPORT VECTOR MACHINES;

EID: 84874557714     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. B. Biggio, B. Nelson, and P. Laskov. Support vector machines under adversarial label noise. In Proceedings of Asian Conference on Machine Learning, pages 97-112, 2011.
2. C. Brodley and M. Friedl. Identifying and eliminating mislabeled training instances. In Proceedings of International Conference on Artificial Intelligence, pages 799-805, 1996.
3. C. Brodley and M. Friedl. Identifying mislabeled training data. Journal of Artificial Intelligence Research, 11:131-167, 1999.
4. C. Burges. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121-167, 1998.
5. C. Campbell, N. Nello, and A. Alex. Query learning with large margin classifiers. In Proceedings International Conference onMachine Learning, pages 111-118, 2000.
6. J. Cao, S. Kwong, and R.Wang. A noise-detection based adaboost algorithm for mislabeled data. Pattern Recognition, 2012.
7. S. Fefilatyev, K. Kramer, L. Hall, D. Goldgof, R. Kasturi, A. Remsen, and K. Daly. Detection of anomalous particles from the deepwater horizon oil spill using the sipper3 underwater imaging platform. In International Conference on Data Mining Workshops, pages 741-748, 2011.
8. P. Frey and D. Slate. Letter recognition using hollandstyle adaptive classifiers. Machine Learning, 6(2):161-182, 1991.
9. R. Kohavi and G. John. Wrappers for feature subset selection. Artificial intelligence, 97(1-2):273-324, 1997.
10. K. Kramer. System for Identifying Plankton from the SIPPER Instrument Platform. Ph.d. dissertation, University of South Florida, 2010.
11. P. Mitra, C. Murthy, and S. Pal. Data condensation in large databases by incremental learning with support vector machines. In Proceedings of International Conference on Pattern Recognition, volume 2, pages 708-711, 2000.
12. P. Mitra, C. Murthy, and S. Pal. A probabilistic active support vector learning algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):413-418, 2004.
13. E. Niaf, R. Flamary, C. Lartizien, and S. Canu. Handling uncertainties in svm classification. In IEEE Statistical Signal Processing Workshop, pages 757-760, 2011.
14. J. Platt et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61-74, 1999.
15. J. Quinlan. Induction of decision trees. Machine learning, 1(1):81-106, 1986.
16. R. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine learning, 37(3):297-336, 1999.
17. G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In Proceedings of International Conference on Machine Learning, volume 282, pages 839-846, 2000.
18. H. Silva and A. Fred. Pairwise vs global multi-class wrapper feature selection. In Proceedings of Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, volume=6, pages=1-6, year=2007.
19. C. Staelin. Parameter selection for support vector machines. Tech. rep. hpl-2002-354r1, Hewlett-Packard Company, 2003.
20. G. Stempfel and L. Ralaivola. Learning svms from sloppily labeled data. Artificial Neural Networks-ICANN 2009, pages 884-893, 2009.
21. J. Thongkam, G. Xu, Y. Zhang, and F. Huang. Support vector machine for outlier detection in breast cancer survivability prediction. Advanced Web and Network Technologies, and Applications, pages 99-109, 2008.
22. I. Tomek. An experiment with the edited nearestneighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, (6):448-452, 1976.
23. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research, 2:45-66, 2002.
24. S. Verbaeten and A. Van Assche. Ensemble methods for noise elimination in classification problems. In Proceedings of International Conference on Multiple Classifier Systems, pages 317-325, 2003.
25. D. Wilson. Asymptotic properties of nearest neighbor rules using edited data. Systems, Man and Cybernetics, IEEE Transactions on, 2(3):408-421, 1972.
26. I.Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.