[A.1] P. S., Bradley, U. M., Fayyad, and O. L., Mangasarian. Mathematical programming for data mining: Formulations and challenges. INFORMS Journal on Computing, 11 (3), 1999.Google Scholar

[A.2] P., Felzenszwalb, R., Girshick, D., McAllester, and D., Ramanan. Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell., 32 (9), 2010.Google Scholar

[A.3] R., Fletcher. Practical Methods of Optimization. Wiley, 1987.

[A.4] B., Karaçali and W., Snyder. On-the-fly multispectral automatic target recognition. In Combat Identification Systems Conference, Jun 2002.

[A.5] D., Li, S. M. R., Azimi, and D. J., Dobeck. Comparison of different neural network classification paradigms for underwater target discrimination. In Proceedings of SPIE, Detection and Remediation Technologies for Mines and Minelike Targets V, volume 4038, pages 334–345, 2000.Google Scholar

[A.6] E., Osuna, R., Freund, and F., Girosi. Training support vector machines: an application to face detection. In Proceedings of CVPR'97, Jun 1997.

[A.7] V., Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.

[A.8] M. H., Yang and B., Moghaddam. Gender classification using support vector machines. In Proceedings of IEEE International Conference on Image Processing, volume 2, pages 471–474, 2000.Google Scholar

[A.9] Y., Yang and X., Liu. Re-examination of text categorization methods. In Proceedings of the 1999 22nd International Conference on Research and Development in Information Retrieval (SIGIR'99), pages 42–49, 1999.

[A.10] N., Zhang, R., Farrell, F., Iandola, and T., Darrell. Deformable part descriptors for fine-grained recognition and attribute prediction. In IEEE International Conference on Computer Vision, 2013.