Self-adaptive Differential Evolutionary Extreme Learning Machine and Its Application in Facial Age Estimation

[1]A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. B, 34(1):621–628, 2004.Search in Google Scholar

[2]A. Lanitis, C. J. Taylor, and T. F. Cootes. Toward automatic simulation of aging effects on face images. TPAMI, 24(4):442–455, 2002.Search in Google Scholar

[3]Huang GB, Zhu QY, Siew CK. Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN2004), vol 2, no 25–29, pp 985–990.Search in Google Scholar

[4]Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501.Search in Google Scholar

[5]Espana-Boquera S, Zamora-Martnez F, Castro-Bleda M J, et al. Efficient BP algorithms for general feedforward neural networks[C]//International Work-Conference on the Interplay Between Natural and Artificial Computation. Springer Berlin Heidelberg, 2007: 327-336.Search in Google Scholar

[6]G. Thatte, U. Mitra, and J. Heidemann, “Parametric methods for anomaly detection in aggregate traffic,” IEEE/ACM Transactions on Networking, vol. 19, no. 2, pp. 512–525, April 2011.Search in Google Scholar

[7]M. Qin and K. Hwang, “Frequent episode rules for internet anomaly detection,” in Proceedings of the Network Computing and Applications, Third IEEE International Symposium. Washington, DC, USA: IEEE Computer Society, 2004, pp. 161–168.Search in Google Scholar

[8]X. He, C. Papadopoulos, J. Heidemann, U. Mitra, and U. Riaz, “Remote detection of bottleneck links using spectral and statistical methods,” Computer Networks, vol. 53, pp. 279–298, February 2009.Search in Google Scholar

[9]W. W. Streilein, R. K. Cunningham, and S. E. Webster, “Improved detec- tion of low-profile probe and denial-of-service attacks,” in Proceedings of the 2001 Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, June 2001.Search in Google Scholar

[10]C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, pp. 273–297, 1995.Search in Google Scholar

[11]G.-B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” International Journal of Machine Leaning and Cybernetics, vol. 2, no. 2, pp. 107–122, 2011.Search in Google Scholar

[12]G. Tandon, “Weighting versus pruning in rule validation for detecting network and host anomalies,” in In Proceedings of the 13th ACM SIGKDD international. ACM Press, 2007.Search in Google Scholar

[13]Y. Liao and V. R. Vemuri, “Use of k-nearest neighbor classifier for intrusion detection,” Computers & Security, vol. 25, pp. 439–448, 2002.Search in Google Scholar

[14]Storn R, Price K (2004) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359Search in Google Scholar

[15]Ilonen J, Kamarainen JI, Lampinen J (2003) Differential evolution training algorithm for feedforward neural networks. Neural Process Lett 17:93–105Search in Google Scholar

[16]Subudhi B, Jena D (2008) Differential evolution and levenberg marquardt trained neural network scheme for nonlinear system identification. Neural Process Lett 27:285–296.Search in Google Scholar

[17]P. Viola and M. J. Jones. Robust real-time face detection. IJCV, 57(2):137–154, 2004.Search in Google Scholar

[18][18] M. Mathias, R. Benenson, M. Pedersoli, and L. Van Gool. Face detection without bells and whistles. In ECCV, 2014, pages 720–735.Search in Google Scholar

[19]S. Escalera, M. Torres, B. Martnez, X. Bar, H. J. Escalante, I. Guyon, G. Tzimiropoulos, C. Corneanu, M. Oliu, M. A. Bagheri, and M. Valstar. Chalearn looking at people and faces of the world: Face analysis workshop and challenge 2016. In ChaLearn Looking at People and Faces of the World, CVPRW, 2016Search in Google Scholar