Cao, P., Zhao, D., & Zaiane, O. (2013, April). An optimized cost-sensitive SVM for imbalanced data learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 280-292). Springer Berlin Heidelberg.
Chen, X. L., Jiang, Y., Chen, M. J., Yu, Y., Nie, H. P., & Li, M. (2012). A Dynamic Cost Sensitive Support Vector Machine. In Advanced Materials Research (Vol. 424, pp. 1342-1346). Trans Tech Publications.
Chen, Y., & Wang, J. Z. (2003). Support vector learning for fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems, 11(6), 716-728.
Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of machine learning research, 2(Dec), 265-292.
Fung, G. M., & Mangasarian, O. L. (2005). Multicategory proximal support vector machine classifiers. Machine learning, 59(1-2), 77-97.
Hwang, J. P., Park, S., & Kim, E. (2011). A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function. Expert Systems with Applications, 38(7), 8580-8585.
Mangasarian, O. L., & Musicant, D. R. (2001). Lagrangian support vector machines. Journal of Machine Learning Research, 1(Mar), 161-177.
Mangasarian, O. L., & Wild, E. W. (2001). Proximal support vector machine classifiers. In Proceedings KDD-2001: Knowledge Discovery and Data Mining.
Nedaie, A., & Najafi, A. A. (2016). Polar support vector machine: Single and multiple outputs. Neurocomputing, 171, 118-126.
Platt, J. C., Cristianini, N., & Shawe-Taylor, J. (1999, November). Large Margin DAGs for Multiclass Classification. In nips (Vol. 12, pp. 547-553).
Pontil, M., & Verri, A. (1998). Support vector machines for 3D object recognition. IEEE transactions on pattern analysis and machine intelligence, 20(6), 637-646.
Qi, Z., Tian, Y., & Shi, Y. (2012). Laplacian twin support vector machine for semi-supervised classification. Neural Networks, 35, 46-53.
Qi, Z., Tian, Y., & Shi, Y. (2013). Robust twin support vector machine for pattern classification. Pattern Recognition, 46(1), 305-316.
Shawe-Taylor, J., & Sun, S. (2011). A review of optimization methodologies in support vector machines. Neurocomputing, 74(17), 3609-3618.
Suykens, J. A., De Brabanter, J., Lukas, L., & Vandewalle, J. (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1), 85-105.
Tian, Y., Qi, Z., Ju, X., Shi, Y., & Liu, X. (2014). Nonparallel support vector machines for pattern classification. IEEE transactions on cybernetics, 44(7), 1067-1079.
Tran, Q. A., Li, X., & Duan, H. (2005). Efficient performance estimate for one-class support vector machine. Pattern Recognition Letters, 26(8), 1174-1182.
Turney, P. D. (1995). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of artificial intelligence research, 2, 369-409.
Vapnik, V. (1998). Statistical Learning Theory, New York, Wiley.
Wan, J. W., Yang, M., & Chen, Y. J. (2012). Cost sensitive semi-supervised Laplacian support vector machine. Acta Electronica Sinica, 40(7), 1410-1415.
Waring, C. A., & Liu, X. (2005). Face detection using spectral histograms and SVMs. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(3), 467-476.
Yang, C. Y., Wang, J. J., Chou, J. J., & Lian, F. L. (2015). Confirming robustness of fuzzy support vector machine via ξ–α bound. Neurocomputing, 162, 256-266.
Zheng, E. H., Li, P., & Song, Z. H. (2006). Cost sensitive support vector machines. Control and decision, 21(4), 473.