International Journal of Applied Mathematics and Machine Learning[1] J. Weston, R. Collobert, F. H. Sinz, L. Bottou and V. Vapnik,
Inference with the Universum, In Proceedings of the 23rd International
Conference on Machine Learning, (2006), 1009-1016.
[2] F. H. Sinz, O. Chapelle, A. Agarwal and B. Schlkopf, An analysis
of inference with the Universum, In Advances in Neural Information
Processing Systems 20 (2008), 1369-1376.
[3] Zhiquan Qi and Yingjie Tian, Twin support vector machine with
Universum data, Neural Networks 36 (2012), 112-119.
[4] C. Shen, P. Wang, F. Shen and H. Wang, Boosting with the
Universum, IEEE Transaction on Pattern Analysis and Machine
Intelligence (2011).
[5] V. Cherkassky, S. Dhar and W. Dai, Practical conditions for
effectiveness of the Universum learning, IEEE Transactions on Neural
Networks 22(8) (2011), 1241-1255.
[6] Dr. 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.
[7] 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.
[8] M. Arun Kumar and M. Gopal, Least squares twin support vector
machines for pattern classification, Expert Systems with Applications
36 (2009), 7535-7543.
[9] Danilo Avilar Silva and Juliana Peixoto Silva, Novel approaches
using evolutionary computation for sparse least square support vector
machines, Neurocomputing 168(30) (2015), 908-916.
[10] A. Jalal Nasiri, Nasrollah Moghadam Charkari and Saeed Jalili,
Least squares twin multi-class classification support vector machine,
Pattern Recognition 48(3) (2015), 984-992.
[11] 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.
[12] Xiaopeng Hua and Shifei Ding, Weighted least squares projection
twin support vector machines with local information, Neurocomputing
160(21) (2015), 228-237.
[13] Shifei Ding and Xiaopeng Hua, Recursive least squares projection
twin support vector machines for nonlinear classification,
Neurocomputing 130(23) (2014), 3-9.
[14] 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.
[15] http://www.ics.uci.edu/mlearn/MLRepository.html
, 1998.
