References
- Z. Wei and D.Miao, ”N-grams based feature selection and text representation for Chinese Text Classification”, Int. J. Comput. Intell.Syst., 2(4), 365–374 (2009).
- L.M.Yang, L.S.Wang, Y.H. Sun and R.Y. Zhang, ”Simultaneous feature selection and classification via Minimax Probability Machine”, Int. J. Comput. Intell. Syst.,3(6), 754–760 (2010).
- ZH.H. Deng, SH.T.Wang, F.L.Chung. A minimax probabilistic approach to feature transformation for multi-class data. Appl soft comput, 13,116–127(2013).
- C. Bhattacharyya, ”Second order cone programming formulations for feature selection”, J.Mach. Learn.Res., 5,1417–1433(2004).
- M. Lobo, L. Vandenberghe, S. Boyd and H. Lebret, ”Applications of second order cone programming”, Linear Algebra Appl., 284, 193–228 (1998).
- G. R. G. Lanckriet, L. E. Ghaoui, C. Bhattacharyya, and M. I. Jordan, ”Minimax probability machine”, Adv. Neural. Inf. Process., 14(2002).
- K. Yoshiyama, Sakurai. A. Manifold-regularized minimax probability machine. In: Partially Supervised Learning, First IAPR TC3 Workshop, 7018, pp. 42–51 (2012).
- V.N.Vapnik, ” Statistical Learning Theory”, New York, Wiley.1998.
- P.D.Tao, L.T.H An, ”Convex analysis approaches to DC programming: theory, algorithms and applications”, Acta. Math, 22(1), 287–367(1997).
- H.A.Le Thi, T.P. Dinh, ”The DC Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems”, Ann. Oper. Res, 133, 23–46(2005).
- H.A.Le Thi, H.M. Le, V.V.Nguyen, P.D.Tao, ”A DC programming approach for feature selection in support vector machines learning”, Adv.Data Anal.Classif., 2,259–278(2008).
- W.Guan, A.Gray. Sparse high-dimensional fractionalnorm support vector machine via DCprogramming. Comput Stat Data Anal .67,136–148(2013).
- Y.Saeki, D.Kuroiwa. Optimality conditions for DC programming problems with reverse convex constraints. Nonlinear Anal. 80, 18–27(2013).
- W. Marshall and I. Olkin, ”Multivariate Chebychev inequalities”, Annals of Math. Stat., 31(4),1001–1014(1960).
- J.F. Sturm, ”Using SeDuMi 1.03, a MATLAB toolbox for optimization over symmetric cones”, (1999). http://www.Unimaas.nl/sturm/software/sedumi.html .
- L.M. Yang, Q.Sun. Recognition of the hardness of licorice seeds using a semi-supervised learning method and near-infrared spectral data. Chemom. Intell. Lab. Syst. 114, 109–115(2012).
- H.Xu, Z.C.Liu, W.S.Cai, X.G. Shao. A wavelength selection method based on randomization test for nearinfrared spectral analysis. Chemom. Intell. Lab. Syst., 97.189–193(2009).