References
- [1] Clark, P.G., Grzymala-Busse, J.W.: Mining incomplete data with lost values and attribute-concept values. In: 2014 IEEE International Conference on Granular Computing (GrC), pp. 49–54 (2014). DOI 10.1109/GRC.2014.698280610.1109/GRC.2014.6982806
- [2] Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)10.1007/BF00994018
- [3] Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annual Eugenics 7, 179–188 (1936)
- [4] Hao, Z., Liu, B., Yang, X.: A comparision of multiclass support vector machine algorithms. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 4221–4226 (2006). DOI 10.1109/ICMLC.2006.25894710.1109/ICMLC.2006.258947
- [5] Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intelligent Systems and their Applications 13(4), 18–28 (1998). DOI 10.1109/5254.70842810.1109/5254.708428
- [6] Jin, B., Tang, Y., Zhang, Y.Q.: Support vector machines with genetic fuzzy feature transformation for biomedical data classification. Information Sciences 177(2), 476 – 489 (2007). DOI https://doi.org/10.1016/j.ins.2006.03.01510.1016/j.ins.2006.03.015
- [7] Lingras, P., Butz, C.: Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification. Information Sciences 177(18), 3782 – 3798 (2007). DOI https://doi.org/10.1016/j.ins.2007.03.02810.1016/j.ins.2007.03.028
- [8] Little, R.J.A., Rubin, D.B.: Statistical analysis with missing data, 2 edn. Wiley–Interscience (2002)
- [9] Ma, Y., Guo, G. (eds.): Support Vector Machines Applications. Springer (2014)
- [10] Nowicki, R.K.: Rough SetBased Classification Systems. Springer International Publishing, Cham (2019). DOI 10.1007/978-3-030-03895-3
- [11] Pawlak, Z.: Information systems — theoretical foundations. Information Systems 6, 205–218 (1981)10.1016/0306-4379(81)90023-5
- [12] Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)10.1007/BF01001956
- [13] Scholkopf, B., Kah-Kay Sung, Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing 45(11), 2758–2765 (1997). DOI 10.1109/78.65010210.1109/78.650102
- [14] Song, H., Ding, Z., Guo, C., Li, Z., Xia, H.: Research on combination kernel function of support vector machine. In: 2008 International Conference on Computer Science and Software Engineering, vol. 1, pp. 838–841 (2008). DOI 10.1109/CSSE.2008.123110.1109/CSSE.2008.1231
- [15] Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer-Verlag (1982)
- [16] Vapnik, V.: Statistical Learning Theory. Wiley (1998)
- [17] Weston, J., Watkins, C.: Multi-class support vector machines. In: Proceedings of ESANN99 (1999)
- [18] Xiao, H., Sun, F., Liang, Y.: Support vector machine algorithm based on kernel hierarchical clustering for multiclass classification. In: 2010 International Conference on Electrical and Control Engineering, pp. 2201–2204 (2010). DOI 10.1109/iCECE.2010.54210.1109/iCECE.2010.542
- [19] Zhang, J., Wang, Y.: A rough margin based support vector machine. Information Sciences 178(9), 2204 – 2214 (2008). DOI https://doi.org/10.1016/j.ins.2007.12.01210.1016/j.ins.2007.12.012