International Journal of Applied Mathematics and Machine Learning[1] V. N. Vapnik, Statistical Learning Theory, Springer, 1998.
[2] V. N. 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] J. A. K. Suykens, L. Lukas and P. Van Dooren et al., Least squares
support vector machine classifiers: A large scale algorithm, In
Proceedings of European Conference Circuit Theory Design (1999),
839-842.
[6] J. A. K. Suykens and J. Vandewalle, Least squares support vector
machine classifiers, Neural Processing Letters 9(3) (1999),
293-300.
[7] M. A. Kumar and M. Gopal, Least squares twin support vector
machines for pattern classification, Expert Systems with Applications
36(4) (2009), 7535-7543.
[8] S. Balasundaram and Kapil, Application of lagrangian twin support
vector machines for classification, Second International Conference on
Machine Learning and Computing (2010), 193-197.
[9] Z. Q. Qi, Y. J. Tian and Y. Shi, Structural twin support vector
machine for classification, Knowledge-Based Systems 43 (2013),
74-81.
[10] Pen Xinjun, Least squares twin support vector hypersphere
(LS-TSVH) for pattern recognition, Expert Systems with Applications 37
(2010), 8371-8378.
[11] S. Ghorai, A. Mukherjee and P. K. Dutta, Nonparallel plane
proximal classifier, Signal Processing 89 (2009), 510-522.
[12] M. A. Kumar and M. Gopal, Application of smoothing technique on
twin support vector machines, Pattern Recognition Letters 29 (2008),
1842-1848.
[13] O. L. Mangasarian and E. W. Wild, Multisurface proximal support
vector classification via generalized eigenvalues, IEEE Transactions
on Pattern Analysis and Machine Intelligence 28(1) (2006), 69-74.
[14] E. Osuna, R. Freund and F. Girosi, Training support vector
machines: An application to face detection, In Proceedings of IEEE
Computer Vision and Pattern Recognition (1997), 130-136.
[15] X. Peng, Twin support vector hypersphere (TSVH) classifier for
pattern recognition, Neural Networks.
[16] Z. Qi, Y. Tian and S. Yong, Robust twin support vector machine
for pattern classification, Pattern Recognition 46(1) (2013),
305-316.
[17] Z. Qi, Y. Tian and S. Yong, Laplacian twin support vector machine
for semisupervised classification, Neural Networks 35 (2012),
46-53.
[18] Y. T. Xu, An improved least squares twin support vector machine,
Journal of Information & Computational Science 4 (2012), 1063-1071.
[19] 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.
[20] 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.
[21] D. Tao, X. Li and X. Wu et al., Supervised tensor learning,
Knowledge and Information Systems 13(1) (2007), 1-42.
[22] D. Cai, X. He, J. R. Wen, J. Han and W. Y. Ma, Support tensor
machines for text categorization, Department of Computer Science,
UIUC. UIUCDCS-R 2006-2714.
[23] C. Hou et al., Multiple rank multi-linear SVM for matrix data
classification, Pattern Recognition 47 (2014), 454-469.
[24] R. Khemchandani, A. Karpatne and S. Chandra, Proximal support
tensor machines, International Journal of Machine Learning and
Cybernetics 4(6) (2013), 703-712.
[25] Xinsheng Zhang, Xinbo Gao and Ying Wang, Twin support tensor
machines for MCs detection, Journal of Electronics (China) 26(3)
(2009), 318-325.
[26] G. Fung and O. L. Mangasarian, Proximal support vector machine
classifiers, In Proceedings of Seventh International Conference on
Knowledge and Data Discovery (2001), 77-86.
[27] 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.
[28] D. Tao, X. Li, W. Hu, S. J. Maybank and X. Wu, Tensor rank one
discriminant analysis: A convergent method for discriminative
multilinear subspace selection, Neurocomputing 71(10-12) (2008),
1866-1882.
[29] 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 modelling, IEEE
Trans. Circuits. Syst. Video Technol. 18(10) (2008), 1397-1410.
[30] D. Tao, J. Sun, J. Shen, X. Wu, X. Li, S. J. Maybank and C.
Faloutsos, Bayesion tensor analysis, IEEE International Joint
Conference on Date of Conference Neural Networks (1-8) (2008),
1402-1409.
[31] J. Sun, D. Tao, S. Papadimitriou, P. Yu and C. Faloutsos,
Incremental tensor analysis: Theory and applications, ACM Transactions
on Knowledge Discovery from Data TKDD 2(3) (2008).
[32] AT&T Labs Cambridge, The Olivetti and Oracle Research Laboratory
Database of Faces, 1994.
http://www.cl.cam.ac.uk/research/dtg/attarchive/fac
edatabase.html
[33] http://cvc.yale.edu/projects/yalefaces/yalefaces.ht
ml.
