Representative Publications

 

 

2023

·       Peiying Li, Yongchang Liu, Jiafeng Zhou, Jieqing Wan, Yunjun Yang, Lei Xu, A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk, Patterns (2023), Patterns 4, Cell press, May 12, 2023,https://doi.org/10.1016/j.patter.2023.100709

·      Yao Zhou, Ruidan Su, Shikui Tu and Lei Xu， A Deep Temporal Factor Analysis Method for Large Scale Financial Portfolio Selection， 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ，Paper Id: ICASSP2023-4350 (Accepted)

·      Weijing Huang, Shikui Tu*, and Lei Xu*. "IA-FaceS: A bidirectional method for semantic face editing." Neural Networks, 2023, 158: 272-292.

·      臧思聪, 涂仕奎*, 徐雷*. “IA-pix2seq：一个实现简笔画可控生成的深度双向学习方法”,《计算机学报》，2023年第46卷第3期

·      Wenqi Guo, Lin Zhang, Shikui Tu, Lei Xu,"Self-Supervised Bidirectional Learning for Graph Matching", AAAI2023(Accepted)

·      Sicong Zang, Shikui Tu and Lei Xu,"Linking Sketch Patches by Learning Synonymous Proximity for Graphic Sketch Representation" AAAI2023(Accepted)

 

2022

·       Lin Zhang, Yangxin Zhu, Shikui Tu, Lei Xu, “A Feature Polymerized Based Two-Level Ensemble Model for Respiratory Sound Classification”, 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2022/10/13, pp238-242.

·      Wenjing Huang, Shikui Tu, Lei Xu, “Deep CNN based Lmser and strengths of two built-in dualities”, Neural Processing Letters, (4), 2022, pp3565-3581

·       盛庆杰, 苏锐丹, 涂仕奎, & 徐雷. 基于 Lmser-in-Lmser 双向网络的人脸素描图像生成方法. 模式识别与人工智能, 35(7), 589-601.

·      Xiangzhe Guo, Shikui Tu, Lei Xu, “Learning to Generate Textual Adversarial Examples”, Artificial Neural Networks and Machine Learning–ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I, pp195-206

·      Ma, Q., Zeng, L., Tu, S., & Xu, L. (2022, December). Kernel Mean Matching with Mahalanobis Distance for Causal Inference of Time-to-event Outcome. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 509-514). IEEE.

·      Hao Qian, Cheng Lin, Dengwei Zhao, Shikui Tu*, Lei Xu*. "AlphaDrug: Protein target specific de novo molecular generation." PNAS Nexus, 1(4), pgac227, 07 October 2022

·      Tianxiang Qin, Shikui Tu*, and Lei Xu*. "IA-NGM: A bidirectional learning method for neural graph matching with feature fusion." Machine Learning, November 01, 2022.  pp 1-27

·      Dengwei Zhao, Shikui Tu, Lei Xu, "Efficient Learning for AlphaZero via Path Consistency." Proceedings of the 39th International Conference on Machine Learning, PMLR 162:26971-26981, 2022.

·       Wenjing Huang, Shikui Tu, and Lei Xu. 2022. Box-FaceS: A Bidirectional Method for Box-Guided Face Component Editing. In Proceedings of the 30th ACM International Conference on Multimedia (MM '22), 2022/10/10, pp 6061-6071.

·      Peng Zhang, Shikui Tu*, Wen Zhang*, Lei Xu. "Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism." Briefings in Bioinformatics (BIB), vol. 23, issue 6, 22 Sep. 2022, bbac403

·      Peiying Li, Shikui Tu*, and Lei Xu*, “Deep Rival Penalized Competitive Learning for Low-resolution Face Recognition”, Neural Networks, vol. 148, pp. 183-193, April 2022

·      Lin Zeng, Haoran Ma, Long Xiang, Shikui Tu, Ying Wang, Liebin Zhao, and Lei Xu,"VentSR: A Self-Rectifying Deep Learning Method for Extubation Readiness Prediction" 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)， pp. 1369-1374, Dec. 6, 2022

·      Peiying Li, Boheng Cao, Shikui Tu, and Lei Xu,"RecurPocket: Recurrent Lmser Network with Gating Mechanism for Protein Binding Site Detection" 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)， pp. 334-339, Dec. 6, 2022

 

2021

·       Zhicheng Wang, Biwei Huang, Shikui Tu*, Kun Zhang*, Lei Xu*, DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 1, pp. 643-650)。

·       Kaixuan Zhao, Shikui Tu*, and Lei Xu*. IA-GM: A Deep Bidirectional Learning Method for Graph Matching. In Proceedings of the AAAI Conference on Artificial Intelligence(Vol. 35, No. 4, pp. 3474-3482).

·       Sicong Zang; Shikui Tu*, and Lei Xu*.  Controllable stroke-based sketch synthesis from a self-organized latent space. Neural Networks, 2021, 137: 138-150.

·      Xie, Q., Tu, S., Wang, G., Lian, Y., & Xu, L. (2021). Discrete biorthogonal wavelet transform based convolutional neural network for atrial fibrillation diagnosis from electrocardiogram. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 4403-4409).

·      Sheng, Q., Tu, S., & Xu, L. (2021). A Consistency Enhanced Deep Lmser Network for Face Sketch Synthesis. In Pacific Rim International Conference on Artificial Intelligence (pp. 127-138). , November  2021, Springer, Cham.

·       Boheng Cao, Shikui Tu, and Lei Xu, Flexible-CLmser: Regularized Feedback Connections for Biomedical Image Segmentation, Regular Paper,  I2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 829-835, doi: 10.1109/BIBM52615.2021.9669446, Dec. 9-12, 2021

·      Fucheng Deng, Shikui Tu, and Lei Xu, Multi-source unsupervised domain adaptation for ECG classification, Regular Paper, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 854-859, doi: 10.1109/BIBM52615.2021.9669755, Dec. 9-12, 2021  

·      Rui Ma, Shikui Tu, Peiying Li, Jiafeng Zhou, Bing Zhao, Jieqing Wan, and Lei Xu, Enriching computed tomography images by projection for robust automated   cerebral aneurysm detection and segmentation, Regular Paper, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 1026-1031, doi: 10.1109/BIBM52615.2021.9669853, Dec. 9-12, 2021

·      Haodong Nie, Shikui Tu, Lei Xu, RecSleepNet: An Automatic Sleep Staging Model Based on Feature Reconstruction, Short Paper, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 9-12, 2021, pp. 1458-1461, doi: 10.1109/BIBM52615.2021.9669687

·       Jinxiong LV, Shikui Tu*, and Lei Xu*. Detection of Phenotype-related Mutations of COVID-19 via the Whole Genomic Data. IEEE/ACM transactions on computational biology and bioinformatics (Early Access), DOI: 10.1109/TCBB.2021.3049836, online: 08 January 2021

 

 

2020

 

·       Lei Xu: Learning deep IA bidirectional intelligence. Frontiers Inf. Technol. Electron. Eng. 21(4): 558-562 (2020)

·       Qingsong Xie, Shikui Tu, Guoxing Wang, Yong Lian, Lei Xu:
Discrete Biorthogonal Wavelet Transform Based Convolutional Neural Network for Atrial Fibrillation Diagnosis from Electrocardiogram. IJCAI 2020: 4403-4409

·       Zhihao Xing, Shikui Tu, Lei Xu:
Solve Traveling Salesman Problem by Monte Carlo Tree Search and Deep Neural Network. CoRR abs/2005.06879 (2020)

·       K. Kuang, L. Li, Z. Geng, Lei. Xu, K. Zhang, B. Liao, H. Huang, P. Ding, W. Miao, Z.Jiang， (2020), Causal Inference,  Engineering， 2020 20(3): Pages 253-263

 

2019

·       Lei Xu (2019), Deep IA-BI and five actions in circling, In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (Eds.),  Lecture Notes in Computer Science, Vol 11935, pp1-15. Springer.

·       Lei Xu (2019), “An overview and perspectives on bidirectional intelligence: Lmser duality, double IA harmony, and causal computation,” IEEE/CAA J. Automatic Sinica, vol. 6, no. 4, pp. 865–893, Jul. 2019.

·       Xie, Q., Tu, S., Wang, G., Lian, Y., & Xu, L. (2019). Feature enrichment based convolutional neural network for heartbeat classification from electrocardiogram. IEEE Access, 7, 153751-153760.

·       Li, P., Tu, S., & Lei Xu (2019),  GAN flexible Lmser for super-resolution, Proc. the 27th ACM International Conference on Multimedia (pp. 756-764). ACM.

·       Jin-Xiong Lv, Shikui Tu, & Lei Xu (2019),  A two-variate phenotype-targeted test for detection of phenotypic biomarkers on breast cancer’. In Bioinformatics and Biomedicine (BIBM), 2019 IEEE International Conference on (pp. 840-845),  Nov.18-21, 019, San Diego, CA

·       Z. Qiang,  X. Gao, S. Tu, and L. Xu (2019), “A k-Dense-UNet for Biomedical Image Segmentation”, In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (Eds.),  Lecture Notes in Computer Science, Vol 11935, pp. 552-562, Springer.

·       Li, M., Tu, S., & Xu, L. (2019). “Computational Decomposition of Style for Controllable and Enhanced Style Transfer”, In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (Eds.), Lecture Notes in Computer Science, Vol 11936, pp. 15-39 Springer.

·       W. Huang, S. Tu, and L. Xu, “Revisit Lmser from a deep learning perspective”, In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (Eds.),  Lecture Notes in Computer Science, Vol 11936, pp. 197-208, Springer.

·       Huang, H. C., Wen, X. Z., Xue, H., Chen, R. S., Ji,J. F., & Xu, L. (2019). Phosphoglucose isomerase gene expression as a prognostic biomarker of gastric cancer. Chinese Journal of Cancer Research, 31(5), 771-+.

·       徐雷、吴飞、孙凌云、涂仕奎、卢策吾，节2.2.3 综合推理与创意人工智能，《中国人工智能2.0发展战略研究》, 浙江大学出版社, 2019年出版。

 

2018

·      Lei Xu (2018), Machine learning and causal analyses for modelling financial and economic data, Springer Nature OA Journal, Applied Informatics, 2018, 5:11, 42 pages， https://doi.org/10.1186/s40535-018-0058-5

·      Lei Xu (2018)， “Deep bidirectional intelligence: AlphaZero, deep IA-search, deep IA-infer, and TPC causal learning”, Springer Nature Open Journal, Applied Informatics, 2018， 5:5， 38pages, https://doi.org/10.1186/s40535-018-0052-y

·      Huang, H. C., Wen, X. Z., Tu, S. K., Ji, J. F., Chen, R. S., & Xu, L. An enviro-geno-pheno state analysis framework for biomarker study. IScIDE 2018, LNCS 11266 (pp 663-671), Springer, 2018. https://link.springer.com/chapter/10.1007/978-3-030-02698-1_58

 

2017

·       Long, W., Tu, S., & Xu L.  (2017). A Comparative Study on Lagrange Ying-Yang Alternation Method in Gaussian Mixture-Based Clustering. Lecture Notes in Computer Science, IScIDE 2017, Vol 10585,pp. 489-499. Springer, Cham.

·      Chen Y., Tu S., Xu L. (2017) Survival-Expression Map and Essential Forms of Survival-Expression Relations for Genes. In: Sun Y., Lu H., Zhang L., Yang J., Huang H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science, Vol 10559, pp. 641-649. Springer, Cham

·      Chen Y., Tu S., Xu L. (2017) The Prognostic Role of Genes with Skewed Expression Distribution in Lung Adenocarcinoma. In: Sun Y., Lu H., Zhang L., Yang J., Huang H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science, Vol 10559, pp 631-640. Springer, Cham

·      Lv J., Tu S., Xu L. (2017) A Comparative Study of Joint-SNVs Analysis Methods and Detection of Susceptibility Genes for Gastric Cancer in Korean Population. In: Sun Y., Lu H., Zhang L., Yang J., Huang H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science, Vol.10559, pp. 619-630. Springer, Cham

·      Lv, J. X., Huang, H. C., Chen, R. S., & Xu, L. (2017). Comparative studies on multivariate tests for joint-SNVs analysis and detection for bipolar disorder susceptibility genes. International Journal of Data Mining and Bioinformatics, 17(4), 2017, pp341-358.

·      Jiang, K. M., Chen, Y. J., Lv, J. X., Lu, B. L., & Xu, L. (2017). Bootstrapping Integrative Hypothesis Test for Identifying Biomarkers that Differentiates Lung Cancer and Chronic Obstructive Pulmonary Disease. Neurocomputing, Elsevier B.V. , Volume 269,  2017, Pages 40-46.

·      徐雷. 人工智能第三次浪潮以及若干认知. 《科学》, 上海科学技术出版社, 2017年 第3期1-5页。

·       H. C., Chen, Xu, L,  “Covariate-time Survival Profiling: A New Perspective for Survival Analysis”, Proc. of BIT’s 2nd International Congress of Genetics-2017,  April 25-27, 2017,  Xi'an, China, p116.

·       徐雷， 信息科学 “金三角” 的故事——纪念常迥先生和程民德先生百年诞辰. 《科学》, 上海科学技术出版社, 2017年 第1期，55-56页。

 

 2016

·       Lv, J. X., Huang, H. C., Chen, R. S., & Xu, L. A comparison study on multivariate methods for joint-SNVs association analysis. In Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on (pp. 1771-1776), 15-18 Dec. 2016, Shenzhen, China

·       L Xu, “A New Multiple Testing Method for Discovering Cancer Biomarkers”,  Proc. of BIT's 9th International Symposium of Cancer Immunotherapy, Nov.16-18, 2016, Nanjing, China, p170.

·       L Xu,“Enviro-geno-pheno state approach and state based biomarkers for differentiation, prognosis, subtypes, and staging”, SpringerOpen Journal, Applied Informatics 2016, 3:4  DOI: 10.1186/s40535-016-0020-3.  

• Lei Xu (2016),  “A new multivariate test formulation: theory, implementation, and applications to genome-scale sequencing and expression”, SpringerOpen Journal, Applied Informatics 2016, 3:1  DOI: 10.1186/s40535-015-0016-4.  

 

2015 

• Kai-Ming Jiang, Bao-Liang Lu, and Lei Xu (2015),  “Bootstrapped Integrative Hypothesis Test, COPD-Lung Cancer Differentiation, and Joint miRNAs Biomarkers”, Lecture Notes in Computer Science: Intelligent Science and Big Data Engineering, Vol .9243, pp. 538–547, Oct. 27, 2015, Springer International Publishing.

• Lei Xu (2015),  “Further advances on Bayesian Ying-Yang harmony learning”, SpringerOpen Journal, Applied Informatics 2015, 2:5  doi:10.1186/s40535-015-0008-4.  
• James T. Kwok, Zhi-Hua Zhou,  Lei Xu (2015), “Machine learning”, Springer Handbook of  Computational Intelligence, J  Kacprzyk & W Pedrycz (eds), Springer-Verlag, Dordrecht Heidelberg, 2015. 
• Lei Xu (2015),  “Bi-linear matrix-variate analyses, integrative hypothesis tests, and case-control studies”, SpringerOpen Journal, Applied Informatics 2015, 2:4  doi:10.1186/s40535-015-0007-5.
• Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng, Lei Xu (2015), “Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning”, in Advances in Independent Component Analysis and Learning Machines, (Bingham, Kaski, Lampinen and Laaksonen, Eds), Elsevier, Amsterdam, 2015.  

 

2014

• Guangyong Chen, Pheng Ann Heng, Lei Xu (2014),  “Projection-embedded BYY learning algorithm for Gaussian mixture-based clustering”, SpringerOpen Journal, Applied Informatics 2015, 2:4  doi:10.1186/s40535-015-0007-5.
• Lei Shi, Zhi-Yong Liu, Shikui Tu, Lei Xu (2014), “Learning Local Factor Analysis versus Mixture of Factor Analyzers with Automatic Model Selection”, Neurocomputing 139 (2014) 3–14.
• Sheng Yujun, Jin Xin, XU Jinhua, …,  , Lei Xu *, Liangdan Sun*, and  Xuejun Zhang*, "Sequencing-based approach identified three new susceptibility loci for psoriasis".  Nature Communications vol.5 no.4331, Nature Publishing Group, 2014.07.09.  (*three corresponding authors).
• Zhi-Yong Liu, Hong Qiao, Li-Hao Jia, and Lei Xu (2014), “A graph matching algorithm based on concavely regularized convex relaxation”,  Neurocomputing 134 (2014) 140–148.
• Zaihu Pang, Shikui Tu, Xihong Wu, Lei Xu (2014), “A comparative study of RPCL and MCE based discriminative training methods for LVCSR”,  Neurocomputing 134 (2014) 53–59.
• Shikui Tu, Lei Xu (2014), “Learning Binary Factor Analysis with Automatic Model Selection”,  Neurocomputing 134 (2014) 149–158.

 

 

2013

• Lei Xu (2013) “Integrative Hypothesis Test and A5 Formulation: Sample Pairing Delta, Case Control Study, and Boundary Based Statistics”, Lecture Notes in Computer Science: Intelligence Science and Big Data Engineering (IScDE 2013)， Vol.8261, pp887-902, 2013.
• Zaihu Pang, Shikui Tu, Xihong Wu, Lei Xu  (2013), “Discriminative GMM-HMM Acoustic Model Selection Using Two-Level Bayesian Ying-Yang Harmony Learning”, Lecture Notes in Computer Science: Intelligent Science and Intelligent Data Engineering, Vol .7751, pp. 719–726, 2013.
• Lei Xu (2013), “Matrix-Variate discriminative analysis, integrative hypothesis testing, and geno-pheno a5 analyzer”, Lecture Notes in Computer Science: Intelligent Science and Intelligent Data Engineering, Vol .7751, pp. 866-875, 2013.

 

2012

• Lei Xu (2012), “Semi-Blind Bilinear Matrix System, BYY Harmony Learning, and Gene Analysis Applications,” Proc. of 6th International Conf. on New Trends in Information Science, Service Science and Data Mining, pp. 661-666, Oct. 23 - 25, 2012,  Taipei.
• Shikui Tu, Dingsheng Luo, Runsheng Chen and Lei Xu (2012), “A Non-Gaussian Factor Analysis Approachto Transcription Network Component Analysis,” IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2012), pp. 404-411, May 9-12, San Diego, California.
• Shikui Tu, Runsheng Chen and Lei Xu (2012), “Transcription Network Analysis by A Sparse Binary Factor Analysis Algorithm,” Journal of Integrative Bioinformatics, vol.9 no.2, pp.198， 2012
• Zhi-Yong Liu, Hong Qiao, and Lei Xu (2012),"A Weight Regularized Relaxation Based Graph Matching Algorithm,” Lecture Notes in Computer Science: Intelligent Science and Intelligent Data Engineering ed. by Yanning Zhang, Zhi-Hua Zhou, Changshui Zhang, Ying Li. pp.9-16. Germany: Springer Berlin / Heidelberg, 2012.03.31.
• Zaihu Pang, Xihong Wu and Lei Xu (2012), “A Comparative Study of RPCL and MCE Based Discriminative Training Methods for LVCSR,”  Lecture Notes in Computer Science: Intelligent Science and Intelligent Data Engineering, Vol 7202/2012,  pp27-34.
• Zhi-Yong Liu, Hong Qiao, and Lei Xu (2012),  “An Extended Path Following Algorithm for Graph-Matching Problem,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.34(7):pp1451-1456, 2012.
• Shikui TU and Lei Xu (2012), “A theoretical investigation of several model selection criteria for dimensionality reduction,” Pattern Recognition Letters , Vol.33:pp1117-1126, 2012.
• Lei Xu (2012), " On essential topics of BYY harmony learning: Current status, challenging issues, and gene analysis applications",  A special issue on Machine learning and intelligence science: IScIDE (C), Journal of Frontiers of Electrical and Electronic Engineering 7(1) (2012) 147–196.

 

2011

• Lei Xu (2011), "Another perspective of BYY harmony learning: representation in multiple layers, co-decomposition of data covariance matrices, and applications to network biology. A special issue on Machine learning and intelligence science: IScIDE2010 (A), Journal of Frontiers of Electrical and Electronic Engineering in China 6(1) (2011) 86–119.
• Shi L, Wang P., Liu H., Lei Xu , and Bao Z(2011), Radar HRRP Statistical Recognition With Local Factor Analysis by Automatic Bayesian Ying-Yang Harmony Learning, IEEE Trans. Signal Process., 2011, 59(2):610–617.
• Lei Xu  (徐雷)(2011), 机器学习之模型选择, <<10000个科学难题--信息科学卷>>, 科学出版社, pp106-110.
• Shikui TU, Lei Xu (2011), "An investigation of several typical model selection criteria for detecting the number of signals",  A special issue on Machine learning and intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical and Electronic Engineering in China 6(2) (2011) 245–255.
• Shikui TU, Lei Xu (2011), "Parameterizations make different model selections: Empirical findings from factor analysis",  A special issue on Machine learning and intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical and Electronic Engineering in China 6(2) (2011) 256–274.
• Shikui Tu, Runsheng Chen and Lei Xu , “A binary matrix factorization algorithm for protein complex prediction“，Proteome Science 9 (Suppl 1), S18， 2011.
• Lei SHI, Shikui TU, Lei Xu (2011), " Learning Gaussian mixture with automatic model selection:A comparative study on three Bayesian related approaches",  A special issue on Machine learning and intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical and Electronic Engineering in China 6(2) (2011) 215–244.
• Penghui WANG, Lei SHI, Lan DU, Hongwei LIU, Lei Xu , Zheng BAO, (2011), "Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning ",  A special issue on Machine learning and intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical and Electronic Engineering in China 6(2) (2011) 300–317.
• Zaihu PANG, Shikui TU, Dan SU, Xihong WU, Lei Xu , (2011), " Discriminative training of GMM-HMM acoustic model by RPCL learning",  A special issue on Machine learning and intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical and Electronic Engineering in China 6(2) (2011) 283–290.

 

2010

• Lei Xu (2010), "Bayesian Ying-Yang system, best harmony learning, and five action circling", A special issue on Emerging Themes on Information Theory and Bayesian Approach, Journal of Frontiers of Electrical and Electronic Engineering in China, 5(3):281–328, 2010.
• Lei Xu (2010), "Machine learning problems from optimization perspective", A special issue for CDGO 07, Journal of Global Optimization, 47, 2010, 369–401.
• Shi, L, Tu S, Lei Xu, Gene clustering by structural prior based local factor analysis model under Bayesian Ying-Yang harmony learning. In: Proceedings of the BIBM 2010 International on Bioinformatics and Biomedicine, Hong Kong, December 18–21, 2010, pp 696-699.
• Tu S, Chen R, Lei Xu,. A binary matrix factorization algorithm for protein complex prediction. In: Proceedings of the BIBM 2010 International Workshop on Computational Proteomics, Hong Kong, December 18–21, 2010, pp 113-118.
• Shi, L., Wang, P., Liu, H., Lei Xu, & Bao, Z. (2010), Radar HRRP statistical recognition with local factor analysis by automatic Bayesian Ying Yang harmony learning, Proc. of 2010 IEEE Intl Conf. on ICASSP, Dallas, TX, USA, March 14 – 19, 2010, 1878-1881.
• Tu, S., & Lei Xu (2010), A study of several model selection criteria for determining the number of signals, Proc. of 2010 IEEE Intl Conf. on ICASSP, Dallas, TX, USA, March 14 – 19, 2010, 1966-1969.
• Su, D, Wu, XH, & Lei Xu (2010), GMM-HMM acoustic model training by a two level procedure with Gaussian components determined by automatic model selection, Proc. of 2010 IEEE Intl Conf. on ICASSP, Dallas, TX, USA, March 14 – 19, 2010, 4890-4893.

 

2009

• Lei Xu (2009), "Learning Algorithms for RBF Functions and Subspace Based Functions", Ch.3 in "Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques", eds. by Olivas, Guerrero, Sober, Benedito, & Lopez, IGI Global publication, pp60-94.
• SUN Ke and Lei Xu (2009).   "Bayesian Ying-Yang learning on orthogonal binary factor analysis".  Neural Network World vol.19 no.5, pp.611-624.
• SUN Ke; TU Shikui; DAVID Yang Gao and Lei Xu (2009).  "Canonical Dual Approach to Binary Factor Analysis".  Lecture Notes in Computer Science 5441 ed. by Adali, T.; Jutten, C.; Romano, J.M.T.; Barros, A.K. . pp.346-353. /Heidelberg, Springer .March, 2003. 
• TU Shikui and Lei Xu (2009)  "Theoretical Analysis and Comparison of Several Criteria on Linear Model Dimension Reduction".  Lecture Notes in Computer Science 5441 ed. by Adali, T.; Jutten, C.; Romano, J.M.T.; Barros, A.K. . pp. 154-162. /Heidelberg, Springer .March, 2003. 

 

2008

• Lei Xu  (2008),  “Independent Subspaces”  in Encyclopedia of Artificial Intelligence,  Edited By: Juan Ramón, Rabuñal Dopico; Julian Dorado; Alejandro Pazos,  IGI Global (IGI)  publishing company,  pp903-912.
• Lei Xu  and E..Oja (2008),  “Randomized Hough Transform”  in Encyclopedia of Artificial Intelligence,  Edited By: Juan Ramón,  Rabuñal Dopico; Julian Dorado; Alejandro Pazos,  IGI Global (IGI)  publishing company,  pp1354-1361.
• Lei Xu  and  Shun-ichi Amari (2008),  “Combining Classifiers and Learning Mixture-of-Experts”  in Encyclopedia of Artificial Intelligence,  Edited By: Juan Ramón,  Rabuñal Dopico; Julian Dorado; Alejandro Pazos,  IGI Global (IGI)  publishing company,  pp318-326.
• Lei Xu (2008), `` Bayesian Ying Yang System, Best Harmony Learning, and Gaussian Manifold Based Family", In J.M. Zurada et al. (Eds.) Computational Intelligence: Research Frontiers, WCCI2008 Plenary/Invited Lectures, LNCS5050,  48–78, 2008. 
• TU Shikui; SHI LEI and XU Lei.  "A Comparative Study on Data Smoothing Regularization for Local Factor Analysis,".  Lecture Notes on Computer Science 5163 ed. by V. Kurkov´a et al. . pp.265–274. Heidelberg, Springer-Verlag Berlin Heidelberg: http://www.springerlink.com/content/uq0x23071164227j/, 2008.09.

·       Qing Shi Gao, Xiao Yu Gao, Lei Xu:
A Probability Theory Perspective on the Zadeh Fuzzy System. Data Mining: Foundations and Practice 2008: 125-137

 

2007

• Lei Xu (2007), Bayesian Ying Yang Learning,  In Scholarpedia, no.18395, http://scholarpedia.org, 2007
• Lei Xu (2007),  A unified perspective on advances of independent subspaces: basic, temporal, and local structures, Proc.6^th.Intel.Conf.Machine Learning and Cybernetics, Hong Kong, 19-22 Aug.2007, 767-776.
• Lei Xu (2007), Rival Penalized Competitive Learning,  In Scholarpedia, no. 19850, http://scholarpedia.org, 2007.
• Lei Xu (2007), ``A unified perspective  and new results on RHT computing, mixture based learning, and multi-learner based problem solving ",  Pattern Recognition,  (40) 2129–2153.
• Lei Xu (2007), `` One-Bit-Matching Theorem for ICA, Convex-Concave Programming on Polyhedral Set, and Distribution Approximation for Combinatorics,  Neural Computation, 19: 546-569. 2007 .

 

2006

 

   •     An, Y.J., Hu, X.L.,  and Lei Xu (2006), ``A Comparative Investigation on Model Selection in Independent Factor Analysis" Journal of Mathematical Modeling and Algorithms 5,   pp.447–473.

   •     Zhi-Yong Liu, Hong Qiao, and Lei Xu, (2006), `` Multisets mixture learning-based ellipse detection ", Pattern Recognition 39, pp731-735, 2006.

 

 

2005

 

• Lei Xu (2005), ``Fundamentals, Challenges, and Advances of Statistical Learning for Knowledge Discovery and Problem Solving: A BYY Harmony Perspective", Proceedings of International Conference on Neural Networks and Brain, Keynote talk, Vol. 1, pp. 24-55, Oct. 13-15, Beijing, China, 2005.
• J. Ma and Lei Xu (2005), ``Asymptotic convergence properties of the EM algorithm with respect to the overlap in the mixture", Neurocomputing, Vol 68, pp105 - 129, 2005. 
• Jinwen Ma , Zhi Yong Liu, and Lei Xu, (2005), `` A Further Result on the ICA One-Bit-Matching Conjecture", Neural Computation, Vol. 17, No. 2, 2005, pp331-334. 

 

2004

 

• Lei Xu (2004), ``Temporal BYY Encoding, Markovian State Spaces, and Space Dimension Determination", IEEE Trans on Neural Networks, Vol. 15, No. 5, pp1276-1295, 2004.
• Lei Xu (2004), ``Advances on BYY Harmony Learning: Information Theoretic Perspective, Generalized Projection Geometry, and Independent Factor Auto-determination", IEEE Trans on Neural Networks, Vol. 15, No. 4, pp885-902, 2004.
• Lei Xu (2004), ``Bayesian Ying Yang Learning (I): A Unified Perspective for Statistical Modeling", Intelligent Technologies for Information Analysis, N. Zhong and J. Liu (eds), Springer, pp615-659, 2004.
• Lei Xu (2004), ``Bayesian Ying Yang Learning (II): A New Mechanism for Model Selection and Regularization", Intelligent Technologies for Information Analysis, N. Zhong and J. Liu (eds), Springer, pp661-706, 2004.
• Kai-Chun Chiu, and Lei Xu (2004), ``Arbitrage Pricing Theory Based Gaussian Temporal Factor Analysis for Adaptive Portfolio Management", Special Issue on Data Mining for Financial Decision Making, The Journal of Decision Support Systems, pp 485- 500, 2004.. 
• Kai-Chun Chiu, and Lei Xu (2004), ``NFA for Factor Number Determination in APT", International Journal of Theoretical and Applied Finance, pp 253-267, 2004.. 
• X. Hu and Lei Xu (2004), ``A comparative investigation on subspace dimension determination", Neural Networks , Vol. 17, pp1051¨C1059, 2004,
• X. Hu and Lei Xu (2004), ``Investigation on Several Model Selection Criteria for Determining the Number of Cluster", Neural Information Processing - Letters and Reviews, Vol. 4, No. 1, pp1-10, July 2004,
• Zhi Yong Liu, Kai Chun Chiu, and Lei Xu, (2004) ``Investigation on Non-Gaussian Factor Analysis", IEEE Signal Processing Letters, Vol. 11, No.7, pp597-600, 2004. 
• Zhi Yong Liu, Kai Chun Chiu, and Lei Xu, (2004) ``One-Bit-Matching Conjecture for Independent Component Analysis", Neural Computation, Vol. 16, No. 2, pp. 383-399. 
• Ma, J, Wang, T, and Lei Xu (2004), ``A gradient BYY harmony learning rule on Gaussian mixture with automated model selection", Neurocomputing, Vol 56, 481 - 487, 2004. 

 

2003

 

• Lei Xu, (2003), ``Data smoothing regularization, multi-sets-learning, and problem solving strategies", Neural Networks, Vol. 16, pp817-825, 2003..
• Lei Xu (2003), `` BYY learning, regularized implementation, and model selection on modular networks with one hidden layer of binary units ", Neurocomputing, Vol. 51, pp 277-301, 2003. Errata to this paper is given here , which is published on Neurocomputing, Vol. 55, pp 405-406, 2003. 
• Lei Xu, (2003), ``Independent Component Analysis and Extensions with Noise and Time: A Bayesian Ying-Yang Learning Perspective ", Neural Information Processing - Letters and Reviews, Vol.1, No.1, pp1-52, 2003. 
• Lei Xu (2003), ``Distribution Approximation, Combinatorial Optimization, and Lagrange-Barrier", Proceedings of International Joint Conference on Neural Networks 2003 (IJCNN '03)}, July 20-24, 2003, Jantzen Beach, Portland, Oregon, pp2354-2359.
• Zhi-Yong Liu, Kai-Chun Chiu, and Lei Xu, (2003), `` Strip Line Detection and Thinning by RPCL-Based Local PCA", Pattern Recognition Letters 24, pp2335¨C2344, 2003.
• Zhi-Yong Liu, Kai-Chun Chiu, and Lei Xu, (2003), " Improved system for object detection and star/galaxy classification via local subspace analysis ", Neural Networks, Vol. 16, pp437¨C451, 2003.
• Zhi-Yong Liu and Lei Xu, (2003) ``Topological Local Principal Component Analysis", Neurocomputing, Vol. 55, No. 3-4, pp. 739-745, 2003.
• Yiu-ming Cheung and Lei Xu (2003), `` Further studies on temporal factor analysis: comparison and Kalman Filter-based algorithm ", Neurocomputing, Vol. 50, 2003, 87-103. 
• Yiu-ming Cheung and Lei Xu (2003), `` Dual Multivariate Auto-Regressive Modeling in State Space for Temporal Signal Separation", IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Volume: 33, No. 3, June 2003, pp386- 398.
• Kei Keung Hung, Yiu-ming Cheung, and Lei Xu (2003), `` An Extended ASLD Trading System to Enhance Portfolio Management", IEEE Transactions on Neural Networks, Vol. 14, No. 2, 2003, 413-425.
• Chiu KC and Lei Xu (2003), ``White noise tests and synthesis of APT economic factors using TFA", Computational Intelligence in Economics and Finance,S-H Chen and P Wang (Ed.), Series on Advanced Information Processing (series editor: L. Jain), Springer Verlag, 2003, pp. 405-419.
• Chiu KC and Lei Xu (2003), ``Optimizing financial portfolios from the perspective of mining temporal structures of stock returns", Lecture Notes in AI, LNAI 2734, Proc. of 2003 Machine Learning and Data Mining in Pattern Recognition, P. Perner and A. Rosenfeld, eds., Springer Verlag, pp266-275.
• Chiu KC and Lei Xu (2003), ``Stock forecasting by ARCH driven gaussian TFA and alternative mixture experts models", Proc. of 3rd International Workshop on Computational Intelligence in Economics and Finance (CIEF'2003), North Carolina, USA, September 26-30, 2003, pp 1096 -1099.
• Chiu KC and Lei Xu (2003), ``On generalized arbitrage pricing theory analysis: empirical investigation of the macroeconomics modulated independent state-space model", Proceedings of 2003 International Conference on Computational Intelligence for Financial Engineering (CIFEr2003), Hong Kong, March 20-23, 2003, pp 139-144.
• Tang, H, Chiu KC, and Lei Xu (2003), ``Finite Mixture of ARMA-GARCH Model For Stock Price Prediction", Proc. of 3rd International Workshop on Computational Intelligence in Economics and Finance (CIEF'2003), North Carolina, USA, September 26-30, 2003, pp.1112-1119.
• Tang, H and Lei Xu (2003), ``MIXTURE-OF-EXPERT ARMA-GARCH MODELS FOR STOCK PRICE PREDICTION", Proc. of 2003 International Conference on Control, Automation, and Systems (ICCAS 2003), October 22-25, 2003 Gyeongju, KOREA, pp402-407.

 

2002

 

• Lei Xu (2002), `` Ying-Yang learning", The Handbook of Brain Theory and Neural Networks, 2nd ed., Michael A. Arbib, The MIT Press, pp1231-1237, 2002. 
• Lei Xu (2002), ``BYY harmony learning, structural RPCL, and topological self-organizing on mixture models", Neural Networks, Vol. 15, pp1125-1151, 2002. 
• Chiu KC and Lei Xu (2002), ``A comparative study of Gaussian TFA learning and statistical tests for determination of factor number in APT", Proceedings of International Joint Conference on Neural Networks 2002 (IJCNN '02), Honolulu, Hawaii, USA, May 12-17, 2002, pp 2243-2248.
• Chiu KC and Lei Xu (2002), ``Stock price and index forecasting by arbitrage pricing theory-based gaussian TFA learning", Lecture Notes in Computer Sciences, Vol.2412, in H. Yin et al., eds., Springer Verlag, 2002, pp366-371.
• Chiu KC and Lei Xu (2002), ``Financial APT-based gaussian TFA learning for adaptive portfolio management", Lecture Notes in Computer Sciences, Vol.2415, in J.R. Dorronsoro (Ed.), Springer Verlag, 2002, pp 1019-1024.

·       Chuangyin Dang, Lei Xu:
A Lagrange Multiplier and Hopfield-Type Barrier Function Method for the Traveling Salesman Problem.Neural Comput. 14(2): 303-324 (2002)

 

2001

 

• Lei Xu (2001), ``BYY Harmony Learning, Independent State Space and Generalized APT Financial Analyses", IEEE Trans. on Neural Networks, Vol. 12, No.4, pp822-849, July, 2001. An Errata to this paper is given on IEEE Trans. on Neural Networks, Vol. 13, No.4, 1023, July, 2002. 
• Lei Xu (2001), ``Best Harmony, Unified RPCL and Automated Model Selection for Unsupervised and Supervised Learning on Gaussian Mixtures, ME-RBF Models and Three-Layer Nets ", International Journal of Neural Systems, Vol.11, No.1, pp3-69, 2001. 
• Lei Xu ,(2001) ``An Overview on Unsupervised Learning from Data Mining Perspective", Advances in Self-Organizing Maps, Nigel Allison, et al eds, Springer-Verlag, pp181-210, 2001. 
• Lei Xu and Irwin King, (2001), ``A PCA approach for fast retrieval of structural patterns in attributed graphs", IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 31, No. 5 , Oct. 2001, pp 812 -817.
• Chuangyin Dang and Lei Xu (2001), ``A Lagrange Multiplier and Hopfield-Type Barrier Function Method for the Traveling Salesman Problem", Neural Computation, Vol. 14 , No. 2, pp303 - 324.
• Chuangyin Dang and Lei Xu (2001), ``A globally convergent Lagrange and barrier function iterative algorithm for the traveling salesman problem", Neural Networks, Vol.14, No.2, pp217-230, 2001.
• Yiu-ming Cheung and Lei Xu (2001), ``Independent Component Ordering in ICA Time Series Analysis'', Neurocomputing, Vol. 41, No. 1-4, pp145-152, 2001.
• Chiu KC and Lei Xu (2001), ``Tests of Gaussian Temporal Factor Loadings in Financial APT", Proc. of 3rd International Conference on Independent Component Analysis and Blind Signal Separation, December 9-12, 2001 - San Diego, California, USA, pp313-318.

 

2000

 

• Lei Xu (2000), `` Temporal BYY Learning for State Space Approach, Hidden Markov Model and Blind Source Separation ", IEEE Trans on Signal Processing, Vol. 48, No. 7, 2132-2144, July, 2000. 
• Lei Xu (2000), `` BYY Learning System and Theory for Parameter Estimation, Data Smoothing Based Regularization and Model Selection ", Neural, Parallel and Scientific Computations, Vol. 8, pp55-82, 2000. 
• C.C.Cheung and Lei Xu, (2000), `` Some Global and Local Convergence Analysis on The Information-Theoretic Independent Component Analysis Approach ", Neurocomputing, Vol.30, pp79-102, 2000.
• J. Ma, Lei Xu and M.I.Jordan (2000), `` Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures ", Neural Computation, Vol.12, No.12, pp2881-2908, 2000. 
• Yiu-ming Cheung and Lei Xu (2000), ``A RPCL-based Approach for Markov Model Identification with Unknown State Number'', IEEE Signal Processing Letters, Vol. 7, No.10, 284-287 (2000).
• Yiu-ming Cheung and Lei Xu (2000), ``Rival Penalized Competitive Learning Based Approach for Discrete-valued Source Separation'', International Journal of Neural Systems, Vol.10, No.6, pp483-490, 2000.

 

1999

 

• Ke Chen, Lei Xu , and Huishen Chi (1999), ``Improved Learning Algorithms for Mixture of Experts in Multiclass Classification", Neural Networks, Vol. 12, pp1229-1252,1999.
• Ouyang Ning, Wing-kai Lam, K. Yamauchi and Lei Xu (1999), ``Using An Improved Back Propagation Learning Method to Diagnose The Sites of Cardiac Hypertrophy", MD Computing, Vol. 16, No.1, pp79-81, 1999.

 

1998

 

• Lei Xu (1998), ``RBF Nets, Mixture Experts, and Bayesian Ying-Yang Learning", Neurocomputing, Vol. 19, No.1-3, pp223-257, 1998. 
• Lei Xu(1998), ``Bayesian Kullback Ying-Yang Dependence Reduction Theory ", Neurocomputing, Vol.22, No.1-3, a special issue on Independence and artificial neural networks , pp81-112, 1998. 
• Lei Xu(1998), ``Bayesian Ying-Yang Dimension Reduction and Determination", Journal of Computational Intelligence in Finance, Vol.6, No.5, a special issue on Complexity and Dimensionality Reduction in Finance . pp6-18, 1998.
• Lei Xu (1998) , ``Rival Penalized Competitive Learning, Finite Mixture, and Multisets Clustering", Proc. Intentional Joint Conference on Neural Networks, Vol., May 5-9, 1998, Anchorage, Alaska. 
• Lei Xu (1998), ``Adaptive RBF Net Algorithms for Nonlinear Signal Learning with Applications to Financial Prediction and Investment", Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP98), May 12-15, 1998, Seattle, WA, Vol. 2, pp1153-1156.
• Lei Xu and W.M.Leung (1998) , ``Cointegration by MCA and modular MCA", Proceedings of IEEE/IAFE 1998 International Conference on Computational Intelligence for Financial Engineering (CIFEr), March 29-31, New York City, pp157-160. 

 

1997

 

• Lei Xu (1997), ``Bayesian Ying-Yang Machine, Clustering and Number of Clusters", Pattern Recognition Letters, Vol.18, No.11-13, pp1167-1178, 1997. 
• Lei Xu (1997), ``Comparative Analysis on Convergence Rate of The EM Algorithm and Its Two Modifications for Gaussian Mixtures", Neural Processing Letters 6, pp69-76, 1997. 
• Lei Xu (1997), ``New Advances on Bayesian Ying-Yang Learning System With Kullback and Non-Kullback Separation Functionals", Proceedings of 1997 IEEE-(INNS) Conference on Neural Networks, Houston, TX, June. 9-12, Vol. 3, pp1942-1947, 1997
• Lei Xu (1997), `` Bayesian Ying-Yang System and Theory as A Unified Statistical Learning Approach (III): Models and Algorithms for Dependence Reduction, Data Dimension Reduction, ICA and Supervised Learning", Invited paper, K.W.Wong, I. King and D.Y.Yeung eds, Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective (TANC97), Springer-Verlag, pp43-60, 1997. 
• Lei Xu (1997), ``Bayesian Ying-Yang System and Theory as A Unified Statistical Learning Approach: (II) From Unsupervised Learning to Supervised Learning and Temporal Modeling ", Invited paper, K.W.Wong, I. King and D.Y.Yeung eds, Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective (TANC97), Springer-Verlag, pp25-42, 1997. 
• Lei Xu (1997), ``Bayesian Ying-Yang System and Theory: An Unified Approach for Statistical Learning: (I) Unsupervised and Semi-Unsupervised Learning", Invited paper, S. Amari and N. Kassabov eds., Brain-like Computing and Intelligent Information Systems, Springer-Verlag, pp241-274, 1997. 
• Lei Xu, C.C. Cheung, H.H. Yang and S.-I. Amari(1997), `` Independent component analysis by the information-theoretic approach with Mixture of Density ", Proc. of 1997 IEEE Intl. Conf on Neural Networks (IEEE-INNS IJCNN97)}, June 9-12, Houston, TX, USA, Vol. III, pp1821-1826(1997).
• Lei Xu, C.C. Cheung, J. Ruan, and S.-I. Amari(1997), ``Nonlinearity and Separation Capability: Further Justification for the ICA Algorithm with A Learned Mixture of Parametric Densities", Invited special session on Blind Signal Separation, Proc. of 1997 European Symp. on Artificial Neural Networks, Bruges, April 16-18, pp291-296(1997). 
• John Sum, C. Leung, Lai-wan Chan and Lei Xu (1997), ``Yet Another Algorithm Which Can Generate Topography Map", IEEE Trans. on Neural Networks, Vol.5, No.5, pp1204-1207, 1997.
• Leung,W.M, Y. M. Cheung and Lei Xu,(1997), `` Application of mixture of experts models to nonlinear financial forecasting",   Nonlinear Financial Forecasting: Proceedings of the First INFFC, R.B.Caldwell ed, Finance & Technology Publishing, pp153-168, 1997.
• Lei Xu and Y.M. Cheung (1997), `` Adaptive supervised learning decision networks for trading and portfolio management", Journal of Computational Intelligence in Finance, Nov/Dec issue, pp11-16, Finance \& Technology Publishing, 1997. 
• Yiu-ming Cheung, W.M. Leung, and Lei Xu (1997), ``Adaptive Rival Penalized Competitive Learning and Combined Linear Predictor Model for Financial Forecast and Investment'', International Journal of Neural Systems, Vol.8, No.5&6, 1997.

 

1996

 

• Lei Xu & M.I.Jordan (1996), ``On convergence properties of the em algorithm for gaussian mixtures", Neural Computation, No.1, Jan, 1996, pp129-151.
• Lei Xu (1996),``A Unified Learning Scheme: Bayesian-Kullback YING-YANG Machine", Advances in Neural Information Processing Systems 8, eds., David S. Touretzky, Michael Mozer, Michael Hasselmo, MIT Press, Cambridge MA, 1996, pp444-450.
• Lei Xu (1996), ``A Maximum Balanced Mapping Certainty Principle for Pattern Recognition and Associative Mapping", Proc. of 1996 World Congress on Neural Networks, Sept. 15-18, 1996, SanDiego, CA, pp.946-949.
• Bailing Zhang, Lei Xu and Minyue Fu (1996), ``Learning Multiple Causes by Competition Enhanced Least Mean Square Error Reconstruction", International Journal of Neural Systems, Vol.7, No.3, pp223-236.
• Yiu-ming Cheung, Zhihong Lai and Lei Xu (1996), ``Adaptive Rival Penalized Competitive Learning and Combined Linear Regressions with Application to Finacial Investment", Proceedings of IEEE/IAFE 1997 International Conference on Computational Intelligence for Financial Engineering (CIFEr), march 24-26, New York City, pp141-147.
• Cheung, Y.M, Leung,W.M, and Lei Xu (1996),``Combination Of Buffered Back-propagation And RPCL-CLP By Mixture-of-Experts Model For Foreign Exchange Rate Forecasting", Neural Networks in Financial Engineering: Proc. of 3rd Intl Conf. on Neural Networks in the Capital Markets, Oct.11-13, London, UK, 1996, World Scientific Pub, pp554-563.
• Adam Krzyzak and Lei Xu (1996), `` Optimal Radial basis Function Nets with application to Nonlinear Function Learning and Classification", Progress in Neural Information Processing: Proc. Intl Conf. on Neural Information Processing (ICONIP96), Sept. 24-27, 1996, pp271-274, Springer-verlag. 

 

1995

 

• Lei Xu (1995), `` Bayesian-Kullback Coupled YING-YANG Machines: Unified Learnings and New Results on Vector Quantization", Proceedings of International Conference on Neural Information Processing, Keynote Speaker, Oct 30-Nov.3, Beijing, China, 1995, pp977-988. 
• Lei Xu, M.I.Jordan & G. E. Hinton (1995), `` An Alternative Model for Mixtures of Experts", Advances in Neural Information Processing Systems 7, eds., Cowan, J.D., Tesauro, G., and Alspector, J., MIT Press, Cambridge MA, 1995, pp633-640.
• Lei Xu & A.L. Yuille (1995), "Robust Principal Component Analysis by Self-Organizing Rules Based on Statistical Physics Approach", IEEE Trans. on Neural Networks, regular paper, Vol.6, No.1, Jan, 1995, pp131-143. 
• Its preliminary version was partially given on Proc. of 1992 IEEE-INNS Intl. Joint Conf. on Neural Networks (IJCNN92), June 7-11, 1992, Baltimore, MA, Vol. I, pp.812-817.
• H.Kalviainen, P.Hirvonen, Lei Xu, & E.Oja, (1995), ``Probabilistic and Non-probabilistic Hough Transforms: Overview and Comparisons", Image and Vision Computing, Vol.5, No. 4, May, 1995.
• M.I.Jordan & Lei Xu (1995), `` Convergence results for the EM approach to mixtures-of-experts architectures'', Neural Networks, Vol.8, No.9, pp1409-1431, the Joint official Journal of International Neural Network Society, European Neural Network Society and Japanese Neural Network Society.
• Alan Yuille, Stelios Smirnakis & Lei Xu (1995), ``Bayesian Self-Organization for visual processing'', Neural Computation 7, pp580-593.
• Lei Xu (1995), `` Vector Quantization by Local and Hierarchical LMSER", Proc. of 1995 Intl Conf. on Artificial Neural Networks, Paris, France, Oct.9-13, 1995, Vol.II, 575-579.
• Lei Xu (1995), `` A Unified Learning Framework: Multisets Modeling Learning", Proceedings of World Congress On Neural Networks, Invited Paper, July 17-21, 1995, Washington, DC, Vol.I, pp35-42.
• Lei Xu (1995)``On The Hybrid LT Combinatorial Optimization: New U-Shape Barrier, Sigmoid Activation, Least Leaking Energy and Maximum Entropy", Proc. 1995 Intl Conf. on Neural Information Processing (ICONIP95), Oct 30 - Nov. 3, Beijing, Vol. I, pp309-312.
• Lei Xu(1995), ``Channel Equalization by Finite Mixtures and The EM Algorithm", Proc. of IEEE Neural Networks and Signal Processing 1995 Workshop, Vol.5, pp603-612, August 31 - September 2, 1995, Cambridge, Massachusetts, USA.
• S.M. Chan, K.M. Lau and Lei Xu (1995), ``Comparison on the Hopfield scheme and the Hybrid Lagrange and Transformation Approaches for Solving the Traveling Salesman Problem", Proc. of 1995 Intl IEEE Symposium on Intelligence in Neural and Biological Systems, May 29-31,1995, Washington DC, USA, IEEE Computer Society Press, pp209-218.

 

1994

 

• Lei Xu (1994), ``Multisets Modeling Learning: An Unified Theory for Supervised and Unsupervised Learning", Proc. of 1994 IEEE International Conference on Neural Networks (ICNN94), Invited Paper, June 26-July 2, 1994,, Orlando, Florida, Vol.I, pp.315-320.
• Lei Xu, Adam Krzyzak & Ching Y. Suen, (1994), `` Associative Switch for Combining Multiple Classifiers", Journal of Artificial Neural Networks, Vol.1, No.1, pp77-100, 1994.
• Lei Xu, A. Krzyzak & A.L. Yuille, (1994), "On Radial Basis Function Nets and Kernel Regression: Statistical Consistency, Convergence Rates and Receptive Field Size", Neural Networks, (the same as the above), Vol.7, No.4, pp609-628, 1994.
• See also the 2nd item in Bayesian Ying-Yang Learning System and Theory. 
• Lei Xu (1994)``Combinatorial Optimization Neural Nets Based on A Hybrid of Lagrange and Transformation Approaches", 1994 Proc. of World Congress on Neural Networks, June 4-9, 1994, SanDiego, CA, Vol.II, 399-404,
• Lei Xu (1994), `` Beyond PCA Learnings: From Linear to Nonlinear and From Global Representation to Local Representation ", Proceedings of International Conference on Neural Information Processing, Invited Paper, Oct 17-20, Seuol, Korea, 1994, pp943-949.
• Lei Xu (1994), `` Theories for Unsupervised Learning: PCA and Its Nonlinear Extensions", Proceedings of IEEE International Conference on Neural Networks 1994, Invited Paper, June 26-July 2, Orlando, Florida, Vol.II, pp1252-1257.
• Lei Xu (1994), ``Signal Segmentation by Finite Mixture Model and EM Algorithm", Proceedings of 1994 Intl. Symposium on Artificial Neural Networks, Dec. 15-17, Tainan, Taiwan, pp453-458.
• Alan L. Yuille, Stelios M. Smirnakis, and Lei Xu, (1994), “Bayesian Self-Organization”, in Cowan, J.D., Tesauro, G., and Alspector, J., eds., Advances in Neural Information Processing Systems 6, Morgan KaufmannPub: San Mateo, CA, pp.1001-1008.

 

1993

 

• Lei Xu (1993), "Least MSE Reconstruction: A Principle for Self-Organizing Nets", Neural Networks, the Joint official Journal of International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, Vol. 6, pp. 627-648, 1993.     
• Its preliminary version was partially given on "Least MSE Reconstruction for Self-Organization: (I)&(II) ", Proc. of 1991 International Joint Conference on Neural Networks, Singapore, Nov., 1991, pp2363-2373. 
• Lei Xu, A. Krzyzak & E.Oja, (1993), "Rival Penalized Competitive Learning for Clustering Analysis, RBF net and Curve Detection", IEEE Trans. on Neural Networks, Vol.4, No.4, pp636-649, 1993.
• Lei Xu & E.Oja, (1993), "Randomized Hough Transform (RHT): Basic Mechanisms, Algorithms and Complexities", Computer Vision, Graphics, and Image Processing : Image Understanding, Vol.57, No.2, March, 1993, pp131-154.

 

1992

 

• Lei Xu, Adam Krzyzak and Ching Y. Suen, (1992), `` Several Methods for Combining Multiple Classifiers and Their Applications in Handwritten Character Recognition", IEEE Trans. on System, Man and Cybernetics, Vol. SMC-22, No.3, pp418-435, 1992.
• Lei Xu, E.Oja & C.Y.Suen, (1992), `` Modified Hebbian Learning for Curve and Surface Fitting ", Neural Networks, the Joint official Journal of International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, Vol.5, 1992, pp441-457.
• Lei Xu, S.Klasa & A.L. Yuille, (1992), ``Recent Advances on Techniques Static Feed-forward Networks with Supervised Learning", International Journal of Neural Systems, Vol.3, No.3, 1992, pp253-290.

 

1991

• Lei Xu, A. Krzyzak and E.Oja, (1991), `` A Neural Net for Dual Subspace Pattern Recognition Methods", International Journal of Neural Systems, Vol.2, No.3, 1991, pp169-184.

 

1990

 

• Lei Xu (1990), `` Adding Learned Expectation into The Learning Procedure of Self-Organizing Maps", International Journal of Neural Systems, Vol.1, No.3, 1990, pp269-283.
• Lei Xu, E.Oja, & P.Kultanen, (1990), `` A New Curve Detection Method: Randomized Hough Transform (RHT)", Pattern Recognition Letters, Vol.11, pp331-338, 1990.

 

1989

• Lei Xu & J.Pearl, (1989), `` Structuring Casual Tree Models with Continuous Variables", in Uncertainty in Artificial Intelligence 3, L.N.Kanal et al ed., Elsevier Science Publishers B.V. (North-Holland), 1989 pp209-219.
• Lei Xu and E.Oja (1989), ``Improved Simulated Annealing, Boltzmann Machine and Attributed Graph Matching", in G.Goos and J.Hartmanis eds., Lecture Notes in Computer Sciences, Vol.412, Springer-Verlag, pp.151-160.