An Efficient Active Training Algorithm for SVM-based Binary Classifiers
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S. Gutta and Q. Cheng, “An efficient training approach for SVM-based binary classifiers,” Proceedings of the International Conference on Smart Technologies (ICST), Chennai, India, Jan. 2011.
A. Smola and B. Schölkopf, “Sparse greedy matrix approximation for machine learning,” Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, USA, June 2000, pp 911-918.
S. Fine and K. Scheinberg, “Efficient SVM training using low-rank kernel representations,” Journal of Machine Learning Research, 2001, pp 243-264.
B. Li, Q. Wang and J. Hu, “A fast SVM training method for very large datasets,” Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 2009.
S. Tong and D. Koller, “Support vector machine active learning with applications to text classification,” Journal of Machine Learning Research, 2001.
H. Shin and S. Cho, “Fast pattern selection for support vector classifiers,” Lecture Notes in Computer Science, Springer, 2003.
R. Koggalage and S. Halgamuge, “Reducing the number of training samples for fast support vector machine classification,” Neural Information Processing - Letters and Reviews, Vol. 2, No. 3, 57–65, 2004.
S. Abe and T. Inoue, “Fast training of support vector machines by extracting boundary data,” Proceedings of the International Conference on Artificial Neural Networks, 2001.
Y. J. Lee and O. L. Mangasarian, “RSVM: Reduced support vector machines,” Proceedings of the First SIAM International Conference on Data Mining, 2001.
J. Cervantes, X. Li and W. Yu, “Support vector machine classification based on fuzzy clustering for large data sets,” Lecture Notes in Computer Science, Vol. 4293, Springer, 2006.
S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004.
V. Vapnik, Estimation of Dependeces Based on Empirical Data. Springer-Verlag, 1982.
J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,” Advances in Kernel Methods - Support Vector Learning, 1998.
Y. Ye, M. J. Todd and S. Mizuno, “An O(√nL)-iteration homogeneous and self-dual linear programming algorithm,” Mathematics of Operations Research, 1994.
S. Hettich and S. D. Bay, The UCI KDD Archive [http://kdd.ics.uci.edu], Irvine, CA: University of California, Department of Information and Computer Science, 1999. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
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