Chan, P. P. K., Liu, W., Chen, D., Yeung, D. S., Zhang F., Wang, X. Z., Hsu, C. C., “Face Liveness Detection based on Flash and no Flash Image Against 2D Spoofing Attack”, IEEE Transactions on Information Forensics and Security, submitted
‧ Chan, P. P. K., Lin, Z., Hu, X., Tseung, E.C.C, Yeung, D. S., “Sensitivity based robust learning for stacked autoencoder against evasion attack”, Neurocomputing, https://doi.org/10.1016/j.neucom.2017.06.032
‧ Chan, P. P. K., He, Z., Li, H., Yeung, D. S., “Data Sanitization against Adversarial Label Contamination Based on Data Complexity”, International Journal of Machine Learning and Cybernetics, Accepted
‧ Sun, B., Ng, W. W. Y., Chan, P. P. K, “Improved Sparse LSSVMS based on the Localized Generalization Error Model”, International Journal of Machine Learning and Cybernetics, 2016
‧ Yeung, D. S., Li, J., Ng, W. W. Y., Chan, P. P. K., “MLPNN training via a multiobjective optimization of training error and stochastic sensitivity”, IEEE Trans. Neural Netw. Learning Syst. 27(5), 978– 992, 2016
‧ Zhang, F., Chan, P.P.K., Biggio, B., Yeung, D.S., Roli, F., “Adversarial feature selection against evasion attacks”, IEEE Trans. Cybernetics 46(3), 766–777, 2016
‧ Biggio, B., Corona, I., He, Z., Chan, P. P. K., Giacinto, G., Yeung, D. S., Roli, F., “One-and-a-half-class multiple classifier systems for secure learning against evasion attacks at test time”, In: Multiple Classifier Systems – 12th International Workshop, MCS 2015, G‥unzburg, Germany, June 29 – July 1, 2015, Proceedings, pp. 168–180, 2015
‧ Chan, P. P. K., Yang, C., Yeung, D. S., Ng, W. W. Y., “Spam filtering for short messages in adversarial environment”, Neurocomputing 155, 167–176, 2015
‧ He, Z., Chan, P. P. K., Yeung, D. S., Pedrycz, W., Ng, W. W. Y., “Quantification of side-channel information leaks based on data complexity measures for web browsing”, International Journal of Machine Learning and, 6(4), 607–619, 2015
‧ Lv, Y., Ng, W. W. Y., Zeng, Z., Yeung, D. S., Chan, P. P. K., “Asymmetric cyclical hashing for large scale image retrieval”, IEEE Trans. Multimedia 17(8), 1225–1235, 2015
‧ Ng, W. W. Y., Li, J., Feng, S., Yeung, D. S., Chan, P. P. K., “Sensitivity based image filtering for multi-hashing in large scale image retrieval problems”, International Journal of Machine Learning and, 6(5), 777–794, 2015
‧ Ng, W. W., Lv Y. M., Zeng Z. Q., Yeung D. S. and Chan, P. P. K., “Sequential conditional entropy maximization semi-supervised hashing for semantic image retrieval”, International Journal of Machine Learning and Cybernetics, pp. 1-16, 2015
‧ Ng, W. W., Lv, Y.M., Yeung, D. S., Chan, P. P. K., “Two-phase mapping hashing”, Neurocomputing, vol. 151, pp. 1423-1429, 2015
‧ Li, J. C., Ng, W. W. Y., Yeung, D. Y., Chan, P. P. K., “Bi-firing deep neural networks”, International Journal of Machine Learning and Cybernetics, Vol 5, pp 73 – 83, 2014
‧ Ng, W. W., He, Z. M., Yeung, D. S. and Chan, P. P. K., “Steganalysis classifier training via minimizing sensitivity for different imaging sources”, Information Sciences, vol. 281, pp. 211-224, 2014
‧ Ng, W. W., Liang X. L., Li J. C., Yeung D. S. and Chan, P. P. K., “LG-Trader: Stock trading decision support based on feature selection by weighted localized generalization error model”, Neurocomputing, vol. 146, pp. 104-112, 2014
‧ Dong, C.R., Ng, W. W., Wang, X.Z., Chan, P. P. K., Yeung, D. S., “An improved differential evolution and its application to determining feature weights in similarity-based clustering”, Neurocomputing, vol. 146, 95-103, 2014
‧ Sun, B., Ng, W. W. Y , Yeung, D. S., Chan, P. P. K., “Hyper-parameter selection for sparse LS-SVM via minimization of its localized generalization error”, International Journal on Wavelets Multiresolution and Information Processing, 11 (03), 2013
‧ Chan P. P. K., Yeung D. S., Ng W. W., Lin C. M., and Liu J. N., “Dynamic fusion method using Localized Generalization Error Model”, Information Sciences, vol. 217, pp. 1-20, 2012
‧ Yeung D. S., Chan P. P. K., Ng W. W., ” Radial Basis Function network learning using localized generalization error bound “, Information Sciences, vol. 179, pp. 3199-3217, 2009
