Information Technology Reference
In-Depth Information
34. J. C. Platt, “Fast training of support vector machines using sequential minimal opti-
mization,” Bernhard Scholkopf, Christopher J. C. Burges, Alexander J. Smola, (Eds.),
in Advances in Kernel Methods: Support Vector Learning , pp. 185-208, Cambridge,
MA: MIT Press, 1999.
35. J. T. Kwok, “Moderating the outputs of support vector machine classifiers,” IEEE
Transactions on Neural Networks , vol. 10, no. 5, pp. 1018-1031, 1999.
36. R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector machines to
imbalanced datasets,” Lecture Notes in Computer Science , vol. 3201, pp. 39-50,
2004.
37. Y. Liu, A. An, and X. Huang, “Boosting prediction accuracy on imbalanced
datasets with SVM ensembles,” Lecture Notes in Artificial Intelligence , vol. 3918,
pp. 107-118, 2006.
38. B. X. Wang and N. Japkowicz, “Boosting support vector machines for imbalanced
data sets,” Lecture Notes in Artificial Intelligence , vol. 4994, pp. 38-47, 2008.
39. Y. Tang and Y. Q. Zhang, “Granular SVM with repetitive undersampling for highly
imbalanced protein homology prediction,” in Proceedings of International Conference
on Granular Computing , pp. 457-460, 2006.
40. X. Hong, S. Chen, and C. J. Harris, “A kernel-based two-class classifier for imbal-
anced datasets,” IEEE Transactions on Neural Networks , vol. 18, no. 1, pp. 28-41,
2007.
41. G. Wu and E. Y. Chang, “Aligning boundary in kernel space for learning imbalanced
dataset,” in Proceedings of International Conference on Data Mining , pp. 265-272,
2004.
42. G. Wu and E. Y. Chang, “KBA: Kernel boundary alignment considering imbalanced
data distribution,” IEEE Transactions on Knowledge and Data Engineering , vol. 17,
no. 6, pp. 786-795, 2005.
43. Y. H. Liu and Y. T. Chen, “Face recognition using total margin-based adaptive fuzzy
support vector machines,” IEEE Transactions on Neural Networks , vol. 18, no. 1,
pp. 178-192, 2007.
44. G. Fung and O. L. Mangasarian, “Multicategory proximal support vector machine
classifiers,” Machine Learning , vol. 59, no. 1, 2, pp. 77-97, 2005.
45. B. Raskutti and A. Kowalczyk, “Extreme re-balancing for SVMs: A case study,”
ACM SIGKDD Explorations Newsletter , vol. 6, no. 1, pp. 60-69, 2004.
46. J. Yuan, J. Li, and B. Zhang, “Learning concepts from large scale imbalanced data
sets using support cluster machines,” in Proceedings of International Conference on
Multimedia , (Santa Barbara, CA, USA), pp. 441-450, ACM, 2006.
47. A. K. Qin and P. N. Suganthan, “Kernel neural gas algorithms with application to
cluster analysis,” in Proceedings of International Conference on Pattern Recognition ,
IEEE Computer Society, (Washington, DC, USA), 2004.
48. P. Li, K. L. Chan, and W. Fang, “Hybrid kernel machine ensemble for imbalanced
data sets,” in Proceedings of International Conference on Pattern Recognition (Hong
Kong, China), pp. 1108-1111, 2006.
49. A. Tashk, R. Bayesteh, and K. Faez, “Boosted Bayesian kernel classifier method for
face detection,” in Proceedings of International Conference on Natural Computation ,
pp. 533-537, IEEE Computer Society, (Washington, DC, USA), 2007.
Search WWH ::




Custom Search