References

LEAST SQUARES SUPPORT MATRIX MACHINES BASED ON BILEVEL PROGRAMMING


[1] V. N. Vapnik, Statistical Learning Theory, Springer, 1998.

[2] V. Vapnik, The Nature of Statistical Learning Theory, New York, NY, USA: Springer-Verlag, 1995.

[3] Jayadeva, R. Khemchandani and S. Chandra, Twin support vector machines for pattern classification, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(5) (2007), 905-910.

[4] Y. H. Shao, C. H. Zhang, X. B. Wang and N. Y. Deng, Improvements on twin support vector machines, IEEE Transactions on Neural Networks 22(6) (2011), 962-968.

[5] M. Arun Kumar and M. Gopal, Least squares twin support vector machines for pattern classification, Expert Systems with Applications 36 (2009), 7535-7543.

[6] Danilo Avilar Silva, Juliana Peixoto Silva and Ajalmar R. Rocha Neto, Novel approaches using evolutionary computation for sparse least square support vector machines, Neurocomputing 168(30) (2015), 908-916.

[7] Jalal A. Nasiri, Nasrollah Moghadam Charkari and Saeed Jalili, Least squares twin multi-class classification support vector machine, Pattern Recognition 48(3) (2015), 984-992.

[8] Yitian Xu, Xianli Pan, Zhijian Zhou, Zhiji Yang and Yuqun Zhang, Structural least square twin support vector machine for classification, Applied Intelligence 42(3) (2015), 527-536.

[9] Xiaopeng Hua and Shifei Ding, Weighted least squares projection twin support vector machines with local information, Neurocomputing 160(21) (2015), 228-237.

[10] Shifei Ding and Xiaopeng Hua, Recursive least squares projection twin support vector machines for nonlinear classification, Neurocomputing 130(23) (2014), 3-9.

[11] Jianhui Guo, Ping Yi, Ruili Wang, Qiaolin Ye and Chunxia Zhao, Feature selection for least squares projection twin support vector machine, Neurocomputing 144(20) (2014), 174-183.

[12] J. Q. Gao, L. Y. Fan, L. Li and L. Z. Xu, A practical application of kernel-based fuzzy discriminant analysis, Int. J. Appl. Math. Comput. Sci. 23(4) (2013), 887-903.

[13] J. Q. Gao, L. Z. Xu, A. Shi and F. C. Huang, A kernel-based block matrix decomposition approach for the classification of remotely sensed images, Applied Mathematics and Computation 228 (2014), 531-545.

[14] C. Hou et al., Multiple rank multi-linear SVM for matrix data classification, Pattern Recognition 47 (2014), 454-469.

[15] R. Khemchandani, A. Karpatne and S. Chandra, Proximal support tensor machines, International Journal of Machine Learning and Cybernetics 4(6) (2013), 703-712.

[16] D. Tao, X. Li, W. Hu, S. J. Maybank and X. Wu, General tensor discriminant analysis and Gabor features for gait recognition, IEEE Trans. Pattern Anal. Mach. Intell. 29(10) (2007), 1700-1715.

[17] D. Tao, M. Song, X. Li, J. Shen, J. Sun, X. Wu, C. Faloutsos and S. J. Maybank, Bayesian tensor approach for 3-D face modeling, IEEE Trans. Circuits Syst. Video Technol. 18(10) (2008), 1397-1410.

[18] D. Tao, J. Sun, J. Shen, X. Wu, X. Li, S. J. Maybank and C. Faloutsos, Bayesian tensor analysis, IEEE International Joint Conference on Date of Conference Neural Networks (2008), 1-8.

[19] Luo Luo, Yubo Xie, Zhihua Zhang and Wu-Jun Li, Support Matrix Machines, Proceedings of the 32nd International Conference on Machine Learning (2015), 938-947.

[20] http://www.cl.cam.ac.uk/research/dtg/attarchive/fac edatabase.html.

[21] http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

[22] Zi Qiang Shi, Ji Qing Han and TieRan Zheng, Soft margin based low-rank audio signal classification, Neural Processing Letters 42(2) (2015), 291-299.

[23] Jian-Feng Cai, J. Emmanuel Cand‘es and Zuowei Shen, A singular value thresholding algorithm for matrix completion, SIAM Journal on Optimization 20(4) (2010), 1956-1982.

[24] Pan-Pan Zheng, Jun Feng, Zhan Li and Ming-quan Zhou, A novel SVD and LS-SVM combination algorithm for blind watermarking, Neurocomputing 142(22) (2014), 520-528.

[25] R. Tyrrell Rochafellar and Roger S-B Wets, Variational Analysis, New York, Springer, 1998.