sets and most of the benchmarks used. Throughout the paper, we discuss our method in the binary classification problem. However, it can be easily extended to multi-class classification problem. A future investigation will focus on theoretical analysis on the generalization ability of GSSVM.

REFERENCES
[1] N. Cristianini and J. Shawe-Taylor, *An Introduction to Support* Vector Machines and Other Kernel-based Learning Methods, 1st ed. Cambridge University Press, 2000.

[2] V. N. Vapnik, *The Nature of Statistical Learning Theory*. SpringerVerlag, 1995.

[3] A. B. Goldberg, N. Fillmore, D. Andrzejewski, Z. Xu, B. Gibson, and X. Zhu, "May all your wishes come true: a study of wishes and how to recognize them," in Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2009, pp. 263–271.

[4] W. S. Noble, "What is a support vector machine?" *Nature Biotechnology*, vol. 24, pp. 1565–1567, 2006.

[5] K. Veropoulos, C. Campbell, and N. Cristianini, "Controlling the sensitivity of support vector machines," in *Proceedings of the International Joint Conference on AI*, 1999, pp. 55–60.

[6] J. Feng and P. Williams, "The generalization error of the symmetric and scaled support vector machines," IEEE Transactions on Neural Networks, vol. 12, 1999.

[7] K. Huang, H. Yang, I. King, and M. R. Lyu, "Learning large margin classifiers locally and globally," in Proceedings of the Twenty-First International Conference on Machine Learning, 2004, p. 51.

[8] D. Yeung, D. Wang, W. Ng, E. Tsang, and X. Wang, "Structured large margin machines: Sensitive to data distributions," *Machine Learning*,
vol. 68, pp. 171–200, 2007.

[9] H. Xue, S. Chen, and Q. Yang, "Structural support vector machine," in Proceedings of the 5th International Symposium on Neural Networks. Springer-Verlag, 2008, pp. 501–511.

[10] T. Xiong and V. Cherkassky, "A combined SVM and LDA approach for classification," in *Proceedings of International Joint Conference* on Neural Networks, 2005.

[11] I. Takuya and A. Shigeo, "Improvement of generalization ability of multiclass support vector machines by introducing fuzzy logic and bayes theory," *Transactions of the Institute of Systems, Control and* Information Engineers, vol. 15, pp. 643–651, 2002.

[12] C.-C. Chang and C.-J. Lin, *LIBSVM: a library for* support vector machines, 2001, software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm.

[13] A. Frank and A. Asuncion, "UCI machine learning repository,"
2010. [Online]. Available: http://archive.ics.uci.edu/ml