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Linked References

1. D. Chen, Z. Liu, X. Ma, and D. Hua, “Selecting genes by test statistics,” Journal of Biomedicine and Biotechnology, vol. 2005, no. 2, pp. 132–138, 2005. View at Publisher · View at Google Scholar · View at PubMed
2. S. Ramaswamy, P. Tamayo, R. Rifkin, et al., “Multiclass cancer diagnosis using tumor gene expression signatures,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 26, pp. 15149–15154, 2001. View at Publisher · View at Google Scholar · View at PubMed
3. N. Jrad, E. Grail-Maës, and P. Beauseroy, “A supervised decision rule for multiclass problems minimizing a loss function,” in Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA '08), pp. 48–53, San Diego, Calif, USA, December 2008. View at Publisher · View at Google Scholar
4. T. M. Ha, “The optimum class-selective rejection rule,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 608–615, 1997. View at Publisher · View at Google Scholar
5. T. Horiuchi, “Class-selective rejection rule to minimize the maximum distance between selected classes,” Pattern Recognition, vol. 31, no. 10, pp. 1579–1588, 1998. View at Publisher · View at Google Scholar
6. E. Grall-Maës, P. Beauseroy, and A. Bounsiar, “Multilabel classification rule with performance constraints,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '06), vol. 3, pp. 784–787, Toulouse, France, May 2006. View at Publisher · View at Google Scholar
7. N. Jrad, E. Grall-Maës, and P. Beauseroy, “Gaussian mixture models for multiclass problems with performance constraints,” in Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN '09), Bruges, Belgium, April 2009.
8. L. Bottou, C. Cortes, J. Denker, et al., “Comparison of classifier methods: a case study in handwritten digit recognition,” in Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR '94), vol. 2, pp. 77–82, Jerusalem, Israel, October 1994. View at Publisher · View at Google Scholar
9. X. Yang, J. Liu, M. Zhang, and K. Niu, “A new multiclass SVM algorithm based on one-class SVM,” in Proceedings of International Conference on Computational Science (ICCS '07), pp. 677–684, Beijing, China, May 2007.
10. P.-Y. Hao and Y.-H. Lin, “A new multi-class support vector machine with multi-sphere in the feature space,” in Proceedings of the 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE '07), vol. 4570 of Lecture Notes in Computer Science, pp. 756–765, Kyoto, Japan, June 2007. View at Publisher · View at Google Scholar
11. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high-dimensional distribution,” Neural Computation, vol. 13, no. 7, pp. 1443–1471, 2001. View at Publisher · View at Google Scholar · View at PubMed
12. D. Tax, One-class classification: concept learning in the absence of counter-examples, Ph.D. thesis, Technische Universiteit Delft, Delft, The Netherlands, 2001.
13. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, Mass, USA, 2001.
14. T. Hastie, S. Rosset, R. Tibshirani, and J. Zhu, “The entire regularization path for the support vector machine,” The Journal of Machine Learning Research, vol. 5, pp. 1391–1415, 2004.
15. A. Rakotomamojy and M. Davy, “One-class SVM regularization path and comparison with alpha seeding,” in Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN '07), pp. 271–276, Bruges, Belgium, April 2007.
16. M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied Linear Statistical Models, McGraw-Hill, New York, NY, USA, 5th edition, 2005.
17. M. B. Brown and A. B. Forsythe, “The small sample behavior of some statistics which test the equality of several means,” Technometrics, vol. 16, no. 1, pp. 129–132, 1974. View at Publisher · View at Google Scholar
18. B. L. Welch, “On the comparison of several mean values: an alternative approach,” Biometrika, vol. 38, no. 3-4, pp. 330–336, 1951.
19. J. Hartung, D. Argaç, and K. H. Makambi, “Small sample properties of tests on homogeneity in one-way Anova and meta-analysis,” Statistical Papers, vol. 43, no. 2, pp. 197–235, 2002. View at Publisher · View at Google Scholar · View at MathSciNet
20. W. G. Cochran, “Problems arising in the analysis of a series of similar experiments,” Journal of the Royal Statistical Society, vol. 4, pp. 102–118, 1937.
21. W. Daniel, Biostatistics: A Foundation for Analysis in the Health Sciences, John Wiley & Sons, New York, NY, USA, 1999.
22. M. Davy, F. Desobry, A. Gretton, and C. Doncarli, “An online support vector machine for abnormal events detection,” Signal Processing, vol. 86, no. 8, pp. 2009–2025, 2006. View at Publisher · View at Google Scholar
23. C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002. View at Publisher · View at Google Scholar · View at PubMed
24. T. R. Golub, D. K. Slonim, P. Tamayo, et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531–527, 1999. View at Publisher · View at Google Scholar
25. J. B. Welsh, P. P. Zarrinkar, L. M. Sapinoso, et al., “Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 3, pp. 1176–1181, 2001. View at Publisher · View at Google Scholar · View at PubMed
26. D. T. Ross, U. Scherf, M. B. Eisen, et al., “Systematic variation in gene expression patterns in human cancer cell lines,” Nature Genetics, vol. 24, no. 3, pp. 227–235, 2000. View at Publisher · View at Google Scholar · View at PubMed
27. U. Scherf, D. T. Ross, M. Waltham, et al., “A gene expression database for the molecular pharmacology of cancer,” Nature Genetics, vol. 24, no. 3, pp. 236–244, 2000. View at Publisher · View at Google Scholar · View at PubMed
28. M. E. Garber, O. G. Troyanskaya, K. Schluens, et al., “Diversity of gene expression in adenocarcinoma of the lung,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 24, pp. 13784–13789, 2001. View at Publisher · View at Google Scholar · View at PubMed
29. A. A. Alizadeh, M. B. Elsen, R. E. Davis, et al., “Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling,” Nature, vol. 403, no. 6769, pp. 503–511, 2000. View at Publisher · View at Google Scholar · View at PubMed
30. E. Grall-Maës and P. Beauseroy, “Optimal decision rule with class-selective rejection and performance constraints,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 99, no. 1, 2009. View at Publisher · View at Google Scholar · View at PubMed