学术论文(Publication List)


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研究专著 (Research Monograph)

  1. He, R., Hu, B.-G., Yuan, X.-T., and Wang, L., Robust Recognition via Information Theoretic Learning, Springer, 2014. (The part on "General Binary Channel (GBC)" in views of binary classifications with a reject option. pp. 41-44. HeBook2014_GBC.pdf)

国际刊物论文 (International Journal Papers)

  1. Ran, Z.-Y., Wang, W. and Hu, B.-G., "On Connections between Rényi Entropy PCA, Kernel Learning and Graph Embedding", Pattern Recognition Letter, Vol. 112, pp. 125-130, 2018.

  2. Fan, X. R., Wang, X., Kang, M., Hua, J., Guo, S., de Reffye, P., and Hu, B.-G., "A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO 2/O 2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models", Computers and Electronics in Agriculture, vol. 148, pp. 280-290, 2018.

  3. Li, H.Y., Dong, W.M.,and Hu, B.-G., "Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks", Journal of Computer Science and Technology, vol. 33, pp. 511-521, 2018.

  4. Xu, G.-B., Hu, B.-G., and Principe, J., “Robust C-Loss Kernel Classifiers”, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, pp. 510-522, 2018.

  5. Ran, Z.-Y., and Hu, B.-G., “Parameter Identifiability in Statistical Machine Learning: A Review”, Neural Computation, Vol. 29, pp. 1151-1203, 2017.

  6. Zhang, Y., Dong, W., Ma, C., Mei, X., Li, K., Huang, H., Hu, B.-G., and Deussen, O., Data-Driven Synthesis of Cartoon Faces Using Different Styles”, IEEE Transactions on Image Processing,Vol. 26, pp. 464-478, 2017.

  7. Xu, G.-B., Gao, Z., Hu, B.-G., and Principe, J.C., "Robust support vector machines based on the rescaled hinge loss function". Pattern Recognition, Vol. 63, pp. 138-148, 2017.

  8. Wu, B.-Y, Hu, B.-G., and Ji, Q., "A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos". Pattern Recognition, Vol. 64, pp. 361-373, 2017.

  9. Ran, Z.-Y., and Hu, B.-G., “Reply to “Reply to ‘Determining structural identifiability of parameter learning machines’””, Neurocomputing, Vol. 218, pp. 318-319, 2016.

  10. Hu, B.-G., and Xing, H.-J. “An Optimization Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications”, Entropy, Vol. 18, pp. 1-19, 2016. (1st version, “Analytical bounds between entropy and error probability in binary classifications”, appeared as arXiv:1205.6602v1[cs.IT] in May 30, 2012, 2nd Version, March 5, 2013).

    Hu_Xing16.pdf

  11. Sheng, K., Dong, W., Kong, Y., Mei, X., Li, J., Wang, C., Huang, F., and Hu, B.-G.,“Evaluating the quality of face alignment without ground truth”, Computer Graphics Forum, Vol. 34, pp. 213-223, 2015.

  12. Fan, X.-R., Kang, M.-Z., de Reffye, P., Heuvelink, E., and Hu, B.-G.,“A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth”, Ecological Modelling, Vol. 312, pp. 363-373, 2015.

  13. Li, C.-G, Mei, X. and Hu, B.-G., Unsupervised ranking of multi-attribute objects based on principal curves”, IEEE Transactions on Knowledge and Data Engineering,Vol. 27, pp. 3404-3416, 2015.  (This work is appeared as arXiv paper on Feb. 19, 2014. http://arxiv.org/abs/1402.4542).

    Source code in Scilab: http://www.escience.cn/people/lichunguo/Resouses.html

  14. Ran, Z.-Y., and Hu, B.-G., "An identifying function approach for determining parameter structure of statistical learning machines", Neurocomputing, Vol. 162, pp. 209-217, 2015.

  15. Wu, B.-Y., Lyu, S., Hu, B.-G., and Ji, Q., "Multi-label learning with missing labels for image annotation and facial action unit recognition". Pattern Recognition, Vol. 48, pp. 2279-2289, 2015.

  16. Zhang, X. and Hu, B.-G., “A New Strategy of Cost-Free Learning in the Class Imbalance Problem”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, pp. 2872-2885, 2014.  (This work is appeared as arXiv paper on July 22, 2013.http://arxiv.org/abs/1307.5730).

  17. Ran, Z.-Y., and Hu, B.-G., “Determining parameter identifiability from the optimization theory framework: A Kullback–Leibler divergence approach”, Neurocomputing, Vol. 142, pp. 307-317, 2014.

  18. Hu, B.-G., “What are the Differences between Bayesian Classifiers and Mutual-Information Classifiers?”, IEEE Transactions on Neural Networks and Learning Systems. Vol. 25, pp. 249-264, 2014. (This work is appeared as arXiv paper, 1st. version on April 30,2011, 2nd. version on March 30,2012, http://arxiv.org/abs/1105.0051v2, but the examples on uniform distributions are not included in the journal paper).

    Erratum.pdf

  19. Wang, W., Hu, B.-G., and Wang, Z.-F., “Globality and Locality Incorporation in Distance Metric Learning”, Neurocomputing, Vol. 129, pp. 185-198, 2014.

  20. Ran, Z.-Y., and Hu, B.-G., “Determining Structural Identifiability of Learning Machines”, Neurocomputing, Vol. 127, pp. 88-97, 2014.
  21. He, R., Zheng, W.-S., Hu, B.-G., and Kong, X., “Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, pp. 35-46, 2013.
  22. Yang, S.-H., and Hu, B.-G., “Discriminative Feature Selection by Nonparametric Bayes Error Minimization”, IEEE Transactions on Knowledge and Data Engineering, Vol. 24, pp. 1422-1434, 2012.
  23. Yuan, X.-T., Hu, B.-G., and He, R., “Agglomerative Mean-Shift Clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 24, pp. 209-219, 2012.
  24. Wu, L., Le Dimet, F.-X., de Reffye, P., Hu, B.-G., Cournède, P.-H., and Kang, M.-Z., “An optimal control methodology for plant growth—Case study of a water supply problem of sunflower”, Mathematics and Computers in Simulation, Vol. 82, pp. 909–923, 2012.
  25. Wang, N. and Hu, B.-G., “Real-Time Simulation of Aeolian Sand Movement and Sand Ripple Evolution: A Method Based on the Physics of Blown Sand”, Journal of Computer Science and Technology, Vol. 27, pp. 135-146, 2012.

    WangNing12.pdf

    DEMO2-17M.wmv

    DEMO1-83M.wmv

  26. Qu, Y.J. and Hu, B.-G., “Generalized Constraint Neural Network Regression Model Subject to Linear Priors”, IEEE Transactions on Neural Networks, Vol. 22, pp. 2447-2459, 2011. Code in Scilab.
  27. Ma, Y.T., Wubs A.M., Mathieu, A., Heuvelink, E., Zhu, J.Y., Hu, B.-G., Cournede, P.H., and de Reffye,P., “Simulation of Fruit-set and Trophic Competition and Optimization of Yield Advantages in SixCapsicum Cultivars Using Functional–structural Plant Modelling”, Annals of Botany, Vol. 107, pp. 793-803, 2011.
  28. He, R., Zheng, W.-S. and Hu, B.-G., “Maximum Correntropy Criterion for Robust Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, pp. 1561-1576, 2011. Code in Scilab.
  29. He, R., Zheng, W.-S. and Hu, B.-G., X.-W. Kong, “A Regularized Correntropy Framework for Robust Pattern Recognition”, Neural Computation, Vol. 23, pp. 2074-2100, 2011.
  30. He, R., Hu, B.-G., Zheng, W.-S. and Kong, X.-W., “Robust Principal Component Analysis based on maximum Correntropy criterion”, IEEE Transactions on Image Processing, Vol. 20, pp. 1840-1494, 2011.
  31. He, R., Hu, B.-G., Yuan, X.-T., and Zheng, W.-S., “Principal component analysis based on non-parametric maximum entropy”, Neurocomputing, Vol. 73, pp. 1485-1852, 2010. Code in Scilab.
  32. Qi, R., Ma, Y.-T., Hu, B.-G., de Reffye, P., Cournade, P-H., “Optimization of source–sink dynamics in plant growth for ideotype breeding: A case study on maize”, Computers and Electronics in Agriculture, Vol. 71, pp. 96-105, 2010.

    Qi10.pdf

  33. Qu, H.-B. and Hu, B.-G., “Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth”, Ecological Informatics, Vol. 4, pp. 163-176, 2009.

    QuHB09.pdf

  34. Hu, B.-G., Qu, H.-B., Wang, Y. and Yang, S.-H., “A generalized constraint neural networks model: Associating partially known relationships for nonlinear regressions”, Information Sciences, Vol. 179, pp. 1929-1943, 2009.

    IS09.pdf

  35. Xing, H.-J. and Hu, B.-G., “Two-Phase Construction of Multilayer Perceptrons Using Information Theory”, IEEE Transactions on Neural Networks, Vol. 20, pp. 715-721, 2009.
  36. Xing, H.-J., Ha, M.-H., Hu, B.-G., and Tian, D.-Z., “Linear feature-weighted support vector machine”, Fuzzy Information and Engineering, Vol. 1, pp. 289-305, 2009.
  37. Yang, S.-H., Hu, B.-G. and Cournède, P.H., “Structural Identifiability of Generalized Constraints Neural Network Models for Nonlinear Regression”, Neurocomputing, Vol. 72, pp. 392-400, 2008.
  38. Kang, M.-Z., Cournède, P.-H., de Reffye, P., Auclair, D., Hu, B.-G.,”Analytical study of a stochastic plant growth model: Application to the GreenLab model”, Mathematics and Computers in Simulation, Vol. 78, pp. 57-75, 2008.
  39. Xing, H.-J. and Hu, B.-G., “An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification”, Neurocomputing, Vol. 71, pp. 1008-1021, 2008.
  40. Teng, J., Jaeger, M., Hu, B.-G., “A Fast Ambient Occlusion Method for Real-Time Plant Rendering”, Journal of Computer Sciences and Technology, Vol. 22, No. 6, pp. 859-866, 2007.

    TengJun07.pdf

  41. Cournède, P.H., Kang, M.Z., Mathieu, A., Yan, H.P., Hu, B.-G. and de Reffye, P, “Structural factorization of plants to compute their functional and architectural growth”, Simulation. Transactions of the Society for Modelling and Simulation International, Vol. 82, No. 7, pp. 427-438, 2006.
  42. Yang, H.-P., de Reffye, P., Pan, C.-H., Hu, B.-G., “Fast construction of plant architectural models based on substructure decomposition”, Journal of Computer Sciences and Technology, Vol. 18, No. 6, pp. 780-787, 2003.

    YanHP03.pdf

  43. Chen, L.-B., Hu, B.-G.,Zhang, L., Li, M.-J., Zhang, H.-J., “Face Annotation for Family Photo Album Management”, International Journal of Image and Graphics, Vol. 3, pp. 81-94, 2003.

    ChenLB02.pdf

  44. Hu, B.-G., Mann, G.K.I. and Gosine, R.G.,“A systematic study of fuzzy PID controllers - Function-based evaluation approach”, IEEE Transactions on Fuzzy Systems, Vol. 9, No. 5, pp. 699-712, 2001.
  45. Mann, G.K.I., Hu, B.-G. and Gosine, R.G., “Time-domain based design and analysis of new PID tuning rules”, IEE Proceedings, D: Control Theory and Applications, Vol. 148, No. 3, pp. 251-261, 2001.
  46. Mann, G.K.I., Hu, B.-G. and Gosine, R.G., “Two-level tuning of fuzzy PID controllers”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 31, No. 2, pp. 263-269, 2001.
  47. Hu, B.-G., Mann, G.K.I. and Gosine, R.G.,“New methodology for analytical and optimal design of fuzzy PID controllers”, IEEE Transactions on Fuzzy Systems, Vol. 7, No. 5, pp. 521-539, 1999.
  48. Mann, G.K.I., Hu, B.-G. and Gosine, R.G., “Analysis of direct action fuzzy PID controller structures”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 29, No. 3, pp. 371-388, 1999.
  49. Hu, B.-G., Mann, G.K.I. and Gosine, R.G., “Control curve design for nonlinear (or fuzzy) proportional actions using spline-based functions”, Automatica, Vol. 34, No. 9 pp. 1125-1133, 1998.

    Automatica98.pdf

  50. Hu, B.-G., Gosine, R.G., Cao, L.X., and de Silva, C.W., “Application of fuzzy classification technique in computer grading of fish product”, IEEE Transactions on Fuzzy Systems, Vol. 6, pp. 144-152, 1998.

    p_tfs98.pdf

  51. Hu, B.-G., Gosine, R.G., “A new eigenstructure method for sinusoidal signal retrieval in a broad band noise - Estimation and pattern recognition”, IEEE Transactions on Signal Processing, Vol. 45, pp. 3073-3083, 1997.

    p_sp97.pdf

  52. Hu, B.-G., Mansour, W.M., and Dokainish, M.A., “Prediction of natural modes of laminated composite plates by finite element technique”, Journal of Sound and Vibrations, Vol. 181, pp. 839-850, 1995.
  53. Hu, B.-G., Dokainish, M.A., and W.M. Mansour, “A Modified MSE Method for viscoelastic systems: A weighted stiffness matrix approach”, Journal of Vibration and Acoustics, Transactions of the ASME, Vol. 117, pp. 226-231, 1995.
  54. Hu, B.-G., Dokainish, M.A., “Damped vibration of laminated composite plates, - Modelling, and finite element analysis”, Finite Elements in Analysis and Design, Vol. 25, pp. 103-124, 1993.

    Hu93.pdf

博士论文 (Ph.D. Thesis)

  1. Hu, B.-G., “Finite element analysis of damped vibrations of laminated composite plates”, McMster University, Canada, November 1992.

    HuBG_Thesis_1992.pdf

开放获取预印本论文 (Open-access Papers on arXiv)

  1. Fan, Y., Lyu, S., Ying, Y. and Hu, B.-G., "Learning with Average Top-k Loss", May 24, 2017 (In:NIPS-2017). (https://arxiv.org/abs/1705.08826).

  2. Fan, Y., Liang, J. He, R., Hu, B.-G., and Lyu, S., "Robust Localized Multi-view Subspace Clustering", May 22, 2017. (https://arxiv.org/abs/1705.07777).

  3. Fan, Y., He, R., Liang, J., and Hu, B.-G., "Self-Paced Learning: an Implicit Regularization Perspective", June 1, 2016 (In:AAAI-2017). (http://arxiv.org/abs/1606.00128).

  4. Cao, L., He, R., and Hu, B.-G., "Locally Imposing Function for Generalized Constraint Neural Networks - A Study on Equality Constraints", Apr. 18, 2016. (http://arxiv.org/abs/1604.05198). (In: 2016 IJCNN).

  5. Mei, X., Qi, H., Hu, B.-G., and Lyu, S., "Improving Image Restoration with Soft-Rounding", Aug. 20, 2015. (http://arxiv.org/abs/1508.05046 (In: 2015 ICCV).

  6. Hu, B.-G., “Information Theory and its Relation to Machine Learning”, Janaury 18, 2015. (http://arxiv.org/abs/1501.04309 (In: Proceedings of the 2015 Chinese Intelligent Automation Conference, 2015, pp 1-11, 2015)

  7. Hu, B.-G., and Dong, W.-M., “A study on cost behaviors of binary classification measures in class-imbalanced problems”, March 26, 2014. (http://arxiv.org/abs/1403.7100).

  8. Li, C.-G., Mei, X., and Hu, B.-G., “Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves”, Feb. 19, 2014. (http://arxiv.org/abs/1402.4542).

  9. X. Zhang, and Hu, B.-G., “A New Strategy of Cost-Free Learning in the Class Imbalance Problem”, July 22, 2013. (http://arxiv.org/abs/1307.5730). (IEEE Transactions on Knowledge and Data Engineering, Vol. 26, pp. 2872-2885, 2014)

  10. Hu, B.-G., and Xing, H.-J. “A New Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications”, (1st version, “Analytical bounds between entropy and error probability in binary classifications”, appeared as arXiv:1205.6602v1[cs.IT] in May 30, 2012), 2nd Version (Four theorems are given). March 5, 2013 . (http://arxiv.org/abs/1303.0943). (Entropy, Vol. 18, pp. 1-19, 2016).

  11. Hu, B.-G., He, R., and Yuan, X.-T. “Information-Theoretic Measures for Objective Evaluation of Classifications” arXiv:1107.1837v1 [cs.CV], 2011. (http://arxiv.org/abs/1107.1837) . Refined and final version published at: Acta Automatica Sinica, Vol.38, No.7, pp.1170-1182, 2012. Code in Scilab.

    zdh12.pdf

  12. Hu, B.-G., “What are the Differences between Bayesian Classifiers and Mutual-Information Classifiers?”, (1st. Version, April 30,2011) arXiv:1105.0051v2 [cs.IT], 2nd. Version (Theorems on Bayesian classifiers are extended to multiple variables. Appendix B for “Tighter bounds between the conditional entropy and Bayesian error in binary classifications” is added, in which Fano’s bound is shown numerically to be very tight. Eq. (B6) should use a notation “↔“, not “=”, for describing the “equivalent relations”). March 7, 2012 . (http://arxiv.org/abs/1105.0051v2). Refined and final version published at: IEEE Transactions on Neural Networks and Learning Systems. Vol. 25, pp. 249-264, 2014.

  13. Wang,Y., and Hu, B.-G., “Derivations of Normalized Mutual Information in Binary Classifications”, arXiv:0711.3675v1 [cs.LG], 2007. (http://arxiv.org/abs/0711.3675). Refined and final version published at: Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD’09), pp. 155-163, 2009.

国内刊物论文 (Chinese Journal Papers)

  1. 冉志勇,胡包钢,“统计机器学习中参数可辨识性研究及其关键问题”, 《自动化学报》,  Vol.  43, No. 10, pp. 1677−1686, 2017.

    zdh17.pdf

  2. Hu, B.-G., He, R., and Yuan, X.-T.,Information-Theoretic Measures for Objective Evaluation of Classifications(分类问题中基于信息论度量的客观评价研究)”,《自动化学报》, Vol.38, No.7, pp.1170-1182, 2012. (原初稿2011年曾在arXiv上发表,本文为修改后的终稿。)Code in Scilab.

    zdh12.pdf

  3. 刘州俊, 胡包钢, “GPU加速的高分辨率DEM图像地形特征线提取算法”,《中国图象图形学报》, 2012, 17,pp. 249-255.

    LiuZJ12.pdf

  4. 王宁, 胡包钢, “一种基于笔触的铅笔素描自动生成方法”,《中国体视学与图像分析》, 2011, 16,pp. 215-222.

    wangn11.pdf

  5. 齐蕊, 胡包钢, “植物昆虫种群动态数学建模研究与展望”,《中国科学:信息科学》, 2010, 40(增刊),pp. 88-103.

    qirui_is2010.pdf

  6. 马韫韬, 朱晋宇, 胡包钢, Heuvelink E, de Reffye P. “基于源-库互反馈的温室青椒坐果时空动态模拟”, 《生态学报》. Vol. 30, No. 24, pp. 7072-7078, 2010.

    mayt10.pdf

  7. Hu, B.-G. and Wang, Y.“Evaluation Criteria Based on Mutual Information for Classifications Including Rejected Class(关于互信息准则在分类(包括拒识类别)问题中的应用)”,Acta Automatica Sinica, Vol.34, No.11, pp.1396-1403, 2008.

    ZDH08.pdf

  8. 王泳, 胡包钢,“应用统计方法综合评估核函数分类能力的研究”,《计算机学报》, Vol.31, No.6, pp.942-952, 2008.

    WangY2008.pdf

  9. 邢红杰,王泳, 胡包钢,“椭球基函数神经网络的混合学习算法”,《模式识别与人工智能》, Vol.21, No.2, pp.148-154, 2008.

    Xing_椭球08.pdf

  10. 胡包钢,王泳,杨双红,曲寒冰,“如何增加人工神经元网络的透明度?”,("How to Add Transparency to Artificial Neural Networks?”, in Chinese with English Abstract)《模式识别与人工智能》, Vol.20, No.1, pp.72-84, 2007.

    How_to_add_transparency_to_ANN.pdf

  11. 胡包钢,“非线性PID控制器研究一比例分量的非线性方法”,《自动化学报》, Vol.32 ,No.2, pp. 219-227, 2006.
    zdh06.pdf
  12. 李晨曦,李昌智,胡包钢,“基于以太网的音乐喷泉控制系统的设计与实现“,《计算机测量与控制》, Vol.14, No.10, pp.1361-1363, 2006.

    LiCengXi06.pdf

  13. 李重,胡包钢,”基于科学计算语言的遗传算法工具箱”,《计算机仿真》, Vol. 22 No. 10, pp. 186-190, 2005.

    LiZhong05.pdf

  14. 王艳妮 ,陈龙斌, 王卫宏, 胡包钢 -,”一种基于语义的图像数据库分类系统”,《计算机应用研究》, Vol. 21 No. 4, pp. 256-260, 2004.

    WangYN04.pdf

  15. 康孟珍,P. de Reffye, 胡包钢, 赵星, “快速构造植物几何的子结构算法”,《中国图形图象学报》, Vol. 9, pp. 79-86,2004.

    KangMZ04.pdf

  16. 赵星,P. de Reffye, 熊范纶,胡包钢,康孟珍,“基于双尺度自动机模型的植物花序模拟”,《计算机学报》, Vol.26 No.1, pp. 116-124, 2003.

    ZhaoX03.pdf

  17. 王矼,胡包钢,腾军,M. Jaeger, “GreenLab模型下的虚拟植物器官造型工具软件”, 《中国图形图象学报》, Vol. 8(A), spec. pp. 847-851,2003.  

    WangJang03.pdf

  18. 曹月东,高东杰,胡包钢,”一类模糊PID控制器的鲁棒优化设计”,《控制与决策》, V0l. 7, No. 1, pp. 73-76, 2002.  

    CaoYueDong02.pdf

  19. 胡包钢, 赵星, 严红平, P. de Reffye, F. Blaise, 熊范纶,,王一鸣, “植物生长建模与可视化-回顾与展望”, 《自动化学报》, Vol. 27, No. 6, pp. 816-835, 2001.

    zdh02.pdf

  20. 赵星,de Reffye P,熊范纶,胡包钢,展志岗,“虚拟植物生长的双尺度自动机模型”,《计算机学报》, Vol.24, No.6,pp. 608-617, 2001.  

    JSJxb01a.pdf

  21. 展志岗, 王一鸣, P. de Reffye, 胡包钢, “冬小麦植株生长的形态构造模型研究”, 《农业工程学报》, Vol. 17, No. 5, pp. 6-10, 2001.  

    ZhangZG01.pdf

  22. 胡包钢, 应浩, “模糊PID控制技术研究发展回顾及其面临的若干重要问题”,《自动化学报》, Vol. 27, No. 4, pp. 567-584, 2001.  

    zdh01b.pdf

  23. 王守唐, 高东杰, 胡包钢. "新型模糊 PID 控制器的稳定性分析." 《自动化学报》, Vol. 26, no. 增刊 B, pp. 61-65, 2000.

    ZDH00.pdf

  24. 胡包钢, G.K.I. Mann, R.G. Gosine,“关于模糊PID控制器推理机维数的研究”,《自动化学报》, Vol. 24, No. 5 , pp. 608-615, 1998.  

    ZDH9805.pdf

国际会议论文 (International Conference Papers)

智能系统及其应用 (Intelligent Systems and Applicaitons)

  1. Zhang, Y., Dong, W.M.,  B.-G. Hu, and Q. Ji, “Classifier Learning With Prior Probabilities for Facial Action Unit Recognition”, in: (CVPR-2018), pp. 5108-5116.

  2. Zhang, Y., Dong, W.M.,  B.-G. Hu, and Q. Ji, “Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation”, in: (CVPR-2018), pp. 2314-2323.

  3. Zhang, Y., Zhao, R., Dong, W.M.,  B.-G. Hu, and Q. Ji, “Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation”, in: (CVPR-2018), pp. 7034-7043.

  4. Fan, Y., Lyu, S., Ying, Y. and Hu, B.-G., "Learning with Average Top-k Loss", In: NIPS-2017. (https://arxiv.org/abs/1705.08826).

  5. Fan, Y., He, R., Liang, J., and Hu, B.-G., "Self-Paced Learning: an Implicit RegularizationPerspective", in: AAAI-2017.

  6. Cao, L., He, R., and Hu, B.-G., "Locally Imposing Function for Generalized Constraint Neural Networks - A Study on Equality Constraints", in: IJCNN-2016, (Appeared first on Apr. 18, 2016. http://arxiv.org/abs/1604.05198 ).

  7. Xu, G.-B., Hu, B.-G., Princepe, J. "Robust Bounded Logistic Regression in the Class Imbalance Problem", in: IJCNN-2016.

  8. Y. Fan, R. He, and B.-G. Hu, "Global and local consistent multi-view subspace clustering", in: ACPR-2015, pp. 564-568.

  9. Mei, X., Qi, H., Hu, B.-G., and Lyu, S., "Improving Image Restoration with Soft-Rounding", In: ICCV-2015.

  10. L. Cao and B.-G. Hu, “Generalized constraint neural network regression model subject to equality function constraints”, in: IJCNN-2015.

  11. Hu, B.-G., “Information Theory and its Relation to Machine Learning”, In: Proceedings of the 2015 Chinese Intelligent Automation Conference, 2015, pp 1-11, 2015 (Appeared first on Janaury 18, 2015. http://arxiv.org/abs/1501.04309 ).

  12. X. Mei, W.-M. Dong, B.-G. Hu, and S. Lyu, “UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints”, in: CVPR-2015, pp. 3753-3761.

  13. C.-G. Li, X. Mei, B.-G. Hu, "Two-Phase Attribute Ordering for Unsupervised Ranking of Multi-attribute Objects", ICDM-Workshops, 2014, pp. 175-182.

  14. Y. Zhang, W. Dong, O. Deussen, F. Huang, K. Li, B.-G. Hu, "Data-driven face cartoon stylization", SIGGRAPH Asia 2014 Technical Briefs, 14I.

    ZhangYong14.pdf

  15. X. Mei, B.-G. Hu, and S. Lyu, “Non-blindimage restoration with symmetric generalized Pareto priors”, in: (ICIP-2014).

    MeiX14.pdf

  16. B. -Y. Wu, Z. Liu, S Wang, B.-G. Hu, and Q. Ji, “Multi-Label Learning with Missing Labels”, in: (ICPR-2014), pp. 1964-1968.

    Wu14ICPR.pdf

  17. Z. -Y. Ran and B.-G. Hu, “An Identifying Function Approach for DeterminingStructural Identifiability of Parameter Learning Machines ”, in: (IJCNN’14), pp. 1593-1599, 2014.

    Ran14IJCNN.pdf

  18. G. -B. Xu, B.-G. Hu, and J. Principe, “An Asymmetric Stagewise Least Square LossFunction for Imbalanced Classification”, in: (IJCNN’14), 2014.

    Xu14.pdf

  19. W. Wang, B.-G. Hu, and Z.-F. Wang, “Efficient and Scalable Information Geometry Metric Learning”, (ICDM-2013), pp. 1217-1222.  

    WangWei13_ICDM.pdf

  20. G.-B. Xu, W., and B.-G. Hu, “Cost-Free Learning for Support Vector Machines with A Reject Option”, (ICDM-OEDM 2013), pp. 817-824.  

    Xu13ICDMW.pdf

  21. B.-Y. Wu, S.-W. Lyu, B.-G. Hu, and Q. Ji, “Simultaneous Clustering and Tracklet Linking for Multi-Face Tracking in Videos”, (ICCV-2013), pp. 2856-2863.  

    WuBY_ICCV13.pdf

  22. C.-G. Li and B.-G. Hu, “Robust Principal Curves based on Maximum Correntropy Criterion”, in: Proceedings of International Conference on Machine Learning and Cybernetics (ICMLC2013), Vol. 2, pp. 615-620, 2013,  

    LiCG13MLC.pdf

  23. B.-Y. Wu,Y.-K. Zhang, B.-G. Hu, and Q. Ji, “Constrained clustering and its application to face clustering in videos”, in: CVPR-2013, pp. 3507-3514, 2013.

    WuBY_CVPR13.pdf

  24. X. Zhang, and B.-G. Hu, “Learning in the class imbalance problem when costs are unknown for errors and rejects”, in: Proceedings of The 12th IEEE International Conference on Data Mining Workshops on Cost Sensitive Data Mining (ICDM-COSTS 2012), pp. 194-201, 2012.

    zhangxw2012.pdf

  25. W. Wang, B.-G. Hu, and Z.-F. Wang, “Discriminating classes collapsing for globality and locality preserving projections”, in: Proceedings of International Joint Conference on Neural Networks (IJCNN’12), pp. 67-74, 2012.

    wangwei12.pdf

  26. Y. -J. Qu and B.-G. Hu, “A Scilab toolbox of nonlinear regression models using a linear solver”, in: Proceedings of 2011 IEEE International Workshop on Open-source Software for Scientific Computation (OSSC-2011), pp. 142-147.  

    QuYJ11scilab.pdf , Scilab Code: GCNNLP.zip

  27. B. -Y. Wu and B.-G. Hu, “Density and Neighbor Adaptive Information Theoretic Clustering”, in: Proceedings of International Joint Conference on Neural Networks (IJCNN’11), pp. 230-237, 2011.

    WuBY11.pdf

  28. R. He, W-S. Zheng, B.-G. Hu, and X.-W. Kong, “Nonnegative Sparse Coding for Discriminative Semi-supervised Learning”, in: Proceedings of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR-11), pp. 2849-2856, 2011,  Code in Scilab.

    heran11cvpr.pdf

  29. B. Dai, B.-G. Hu, and G. Niu, “Bayesian Maximum Margin Clustering”, in: ICDM-2010, pp. 108-117, 2010.

    daboicdm10.pdf

  30. B. Dai and B.-G. Hu, “Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering”, in: ACML-2010, pp. 47-62, 2010.

    daboacml10.pdf

  31. R. He, B.-G. Hu, W-S. Zheng, and Y.Q. Guo, “Two-Stage Sparse Representation for Robust Recognition on Large-Scale Database”, in: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), pp. 475-480, 2010, Code in Scilab.

    heran10aaai.pdf

  32. Y. J. Qu, B. Dai and B.-G. Hu, “Neural-network based regression model with prior from ranking information”, in: Proceedings of International Joint Conference on Neural Networks (IJCNN’10), pp. 3005-3012, 2010.

    QuYaJun2010.pdf

  33. S.-H. Yang, H.Y. Zha and B.-G. Hu, “Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora”, in: Advances in Neural Information Processing Systems 23 (NIPS’09).

    YangNIPS09.pdf

  34. B.-G. Hu, “Information Measure Toolbox for Classifier Evaluation on Open Source Software Scilab”, in: Proceedings of 2009 IEEE International Workshop on Open-source Software for Scientific Computation (OSSC-2009), pp. 179-184. Code in Scilab.

    ossc09hubgfinal.pdf

  35. R. Qi, B.-G. Hu, and P-H. Cournede, “PSOTS: A Particle Swarm Optimization Toolbox in Scilab”, in: Proceedings of 2009 IEEE International Workshop on Open-source Software for Scientific Computation (OSSC-2009), pp. 171-114.
    ossc09qirui.pdf
  36. Y. Wang, and B.-G. Hu, “Derivations of Normalized Mutual Information in Binary Classifications”, in: Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD’09), pp. 155-163, 2009.
  37. C.-T. Liu and B.-G. Hu, “Mutual Information Based on Renyi's Entropy Feature Selection”, in: Proceedings of 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009), pp. 816-820.

    LiuCT09.pdf

  38. R. He, B.-G. Hu and X.-T. Yuan “Robust Discriminant Analysis Based on Nonparametric Maximum Entropy”, Asian Conference on Machine Learning (ACML-2009), 2009.

    HeRan09.pdf

  39. Y.-J. Qu and B.-G. Hu, “RBF Networks for Nonlinear Models subject to Linear Constraints”, The 2009 IEEE International Conference on Granular Computing (GrC 2009), pp. 482-487.

    QuYJ09GRC.pdf

  40. S.-H. Yang, H.Y. Zha, S.K. Zhou and B.-G. Hu, “Variational Graph Embedding for Globally and Locally Consistent Feature Extraction”, ECML-PKDD 2009, Part II, LNAI 5782, pp. 538-553, 2009., Springer Press.

    YangECML09.pdf

  41. X.-T. Yuan, and B.-G. Hu, “Robust Feature Extraction via Information Theoretic Learning”, The 26th International Conference on Machine Learning (ICML 2009).

    yuanicml09.pdf

  42. X.-T. Yuan, B.-G. Hu, and R. He, “Agglomerative Mean-Shift Clustering via Query Set Compression”, 2009 SIAM International Conference on Data Mining (SDM’09).Code in Scilab.

    yuan_SDM09.pdf

  43. H.-J. Xing, M.-H. Ha, D.-Z. Tian, and B.-G. Hu, “A novel support vector machine with its features weighted by mutual information”, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN’08), Hong Kong, China, June 1-8, 2008.

    Xing08_IJCNN.pdf

  44. S.-H. Yang and B.-G. Hu, “A Stagewise Least Square Loss Function for Classification”, in: Proceeding of the 2008 SIAM International Conference on Data Mining (SDM’08), pp.120-131, SIAM Press.

    yangSDM08.pdf

  45. S.-H. Yang and B.-G. Hu, “Feature Selection by Nonparametric Bayes Error Minimization”, The 12th Pacific-Asian Conference on KDD (PAKDD’08), LNAI, Vol.5012, pp.417-428, Springer. (Recipient of the Best Student Paper Award)

    YangSH_PAKDD08_L.pdf

  46. S.-H. Yang, Y.-J. Yang and B.-G. Hu, “Sparse Kernel-based Feature Weighting”, The 12th Pacific-Asian Conference on KDD (PAKDD’08), LNAI, Vol.5012, pp.813-820, Springer.

    YangSH_PAKDD08.pdf

  47. Y.-J. Yang, S.-H. Yang and B.-G. Hu, “Fighting WebSpam: Detecting Spam on the Graph Via Content and Link Features”, The 12th Pacific-Asian Conference on KDD (PAKDD’08), LNAI, Vol.5012, pp.1049-1055, Springer.
  48. S.-H. Yang and B.-G. Hu, “Efficient Feature Selection in the Presence of Outliers and Noises”. Asian Conference on Information Retrieval (AIRS’08), LNCS, pp.188-195, Springer.

    yang_sh_airs08.pdf

  49. Mei, X., Decaudin, Ph., and B.-G., Hu., “Fast Hydraulic Erosion Simulation and Visualization on GPU”, in:15th Pacific Conference on Graphics and Applications (PG’07), IEEE, pp. 47-56, 2007. (Project)  
  50. Y.-J. Yang and B.-G. Hu, Pairwise Constraints-Guided Non-negative Matrix Factorization for Document Clustering, IEEE/WIC/ACM International Conference on Web Intelligence (WI’07), Silicon Valley, USA, Nov. 2-5, 2007, pp. 250-256.

    YangYJ07.pdf

  51. H.-B. Qu, and B.-G. Hu., “Variational Bayes Inference for Generalized Associative Functional Networks”, in: Proceedings of the Twentieth International Joint Conference on Neural Networks (IJCNN’07), Orlando, Florida, USA, August 12-17, 2007.

    QuHB_IJCNN07.pdf

  52. L.-J. Chen and B.-G. Hu., “An Implementation Of Web Based Query By Humming System”, in: Proceedings of International Conference on Multimedia and Expo 2007, pp. 1467-1470.

    ChenLJ07.pdf

  53. T. Peng, I. Jermyn, V. Prinet, J. Zerubia, and B.-G. Hu., “A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images”. British Machine Vision Conference 2007 (BMVC-2007), September 2007, Warwick, UK.

    peng07BMVC.pdf

  54. T. Peng, I. Jermyn, V. Prinet, J. Zerubia, and B.-G. Hu., “Urban Road Extraction from VHR Images Using a Multiscale Approach and a Phase Field Model of Network Geometry”. Proc. 4th IEEE GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (URBAN-2007), April 2007, Paris, France.

    peng07urban.pdf

  55. S.-H, Yang and B. -G. Hu, “Reformulated Parametric Learning Based on Ordinary Differential Equations”, ICIC 2006, LNAI 4114, pp. 256-267, 2006.

    YangSH2006.pdf

  56. B.-G. Hu, Han-Bing Qu, and Yong Wang, “Associating Neural Networks with Partially known Relationships for Nonlinear Regressions”, ICIC 2005, Part I, LNCS 3644, pp. 737 - 746, 2005.
  57. Xing, H-J., Yang, Y-J., Wang, Y., Hu, B. -G., “Sparse Kernel Fisher Discriminant Analysis”, Springer Lecture Notes in Computer Science, LNCS 3496, pp. 824-830, 2005.
  58. Xing, H-J., Hu, B.-G., “Gauss-Chebyshev Neural Networks“, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp. 4112-4115, Guangzhou, China, August 18-21, 2005.
  59. Hu, B.-G., “Numerical studies of nonlinear PID controllers using scilab/scicos”, International Conference on Instrumentation, Control and Information Technology, Okayama, Japan, August 8-10, 2005.
  60. Chong Chen, Changzhi Li, and B.-G.Hu, “Interactive Design of Postures for Articulated Character Dancing Animation”, In Proceedings of International Conference on Computer Graphics & Vision (GraphiCon-2004), pp. 64-70, Moscow, September 2004.

    ChenCong2004.pdf

  61. Hu, B.-G., Xing, H.-J., and Yang, Y.-J., “Geometric interpretation of nonlinear approximation capability for feedforward neural networks”, in: Advances in Neural Networks - ISNN2004, Yin, F. Wang, J. and Guo, C., (eds), Springer, 2004, Part 1, pp. 7-13.
  62. M.F. Aly, B.-G. Hu, S.C. and Veldhuis,  “Fuzzy Non Linear PI Controller for High Performance”, Proceeding of IMECE-ASME, Nov. 15-21 Washington D.C. USA, 2003.

  63. Yanni Wang, B.-G. Hu, “Hierarchical Image Classification Using Support Vector Machines”, Proceeding of The 5th Asian Conference on Computer Vision, (ACCV-2002) Melbourne, Australia, pp.640-645, 2002.
  64. Yanni Wang, Longbin Chen, B.-G. Hu, “Semantic Extraction of the Building Images using Support Vector Machines”, Proceeding of The First International Conference on Machine Learning and Cybernetics, Beijing, 2002, pp.1608-1613.
  65. Longbin Chen, Yanni Wang, B.-G. Hu, “Kernel Based Similarity Learning”, Proceeding of The First International Conference on Machine Learning and Cybernetics, 2002, Beijing, pp.2152-2156.
  66. Cao, Y.-D., Hu, B.-G., and Gao, D.-J., “Genetic-based robust optimal design for one-input fuzzy PID cotrollers”, Proceedings of 2001 IEEE International Conference on Systems, Man, and Cybernetics, Tucson, USA, Oct. 7-10, 2001, pp. 2263-2268.
  67. Liang, L.C., Hu, B.-G., Wang, Z., and Ye, F.J., “Cell tracking using the condesation algorithm”, Proceedings of The 6th International Conference on Control, Automation, Robotics and Vision, pp. 183-187, Singapore, Dec. 6-8, 2000.
  68. Wang, Z. Hu, B.-G., Liang, L.C., and Ji, Q., “Cell detection and tracking for micromanipulation vision system of cell-operation robot”, Proceedings of 2000 IEEE International Conference on Systems, Man, and Cybernetics, Nashville, Tennessee, USA, Oct. 8-11, 2000, pp. 1592-1597.
  69. Shao, H.-F., Hu, B.-G. And Zhu, Z.-L., “A case study of one-to-two mapping fuzzy PD controllers on inverted pendulum”, Proceedings of the Eighth IEEE International Conference on Fuzzy Systems, Seoul, South Korea, Aug. 22-25, 1999, pp. 424-429.
  70. Hu, B.-G., Mann, G.K.I. and Gosine, R.G., 1999, “How to evaluate fuzzy PID controllers without using process information?”, Proceedings of the 14th World Congress-IFAC, Vol. K, pp. 177-182, Beijing, China, July 5-9, 1999.
  71. Hu, B.-G., Mann, G.K.I. and Gosine, R.G., 1998, “Nonlinearity variation analysis of one-input fuzzy PID controllers”, Proceedings of 1998 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, USA, Oct. 11-14, 1998, pp. 1916-1921. Mann, G.K.I., Hu, B.-G. and Gosine, R.G., 1998, “Derivation and analysis of three-input inference for fuzzy PID controllers”, Proceedings of 1998 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, USA, Oct. 11-14, 1998, pp. 1910-1915.
  72. Mann, G.K.I., Hu, B.-G. and Gosine, R.G., 1998, “How fuzzy PID controllers are manually tuned for better performance?” JCIC`98 Proceedings Vol. I, pp. 171-174, Sixth International Conference on Fuzzy Theory and Technology, Research Triangle Park, NC, USA, Oct. 23-28, 1998.
  73. Mann, G.K.I., Hu, B.-G. and Gosine, R.G., 1998, “Evaluation of fuzzy reasoning schemes for fuzzy controllers”, JCIC`98 Proceedings Vol. I, pp. 175-178, Sixth International Conference on Fuzzy Theory and Technology, Research Triangle Park, NC, USA, Oct. 23-28, 1998.
  74. Mann, G.K.I., Hu, B.-G., and Gosine, R.G., “Analysis and development of a new PID controller tuning rules for first-order processes”, Pre-Prints of IFAC/IEEE International Symposium on Artificial Intelligence in Real-Time Control, Kuala Lumpur, Malaysia, Sept. 22-25, 1997, pp. 203-209.
  75. Hu, B.-G., Mann, G.K.I. and Gosine, R.G., “Theoretic and genetic designs of a three-rule fuzzy PI controller”, Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, Barcelona, Spain, July 1-5, 1997, pp. 489-496.
  76. Mann, G.K.I., Hu, B.-G., and Gosine, R.G., “Analysis and performance evaluation of linear-like fuzzy PI and PID controllers”, Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, Barcelona, Spain, July 1-5, 1997, pp. 383-390.
  77. Hu, B.-G., Mann, G.K.I. and Gosine, R.G., “Theoretic derivation of control actions for three-rule fuzzy PID controllers”, Proceedings, IEEE 1997 Canadian Conference on Electrical and Computer Engineering, May 25-28, 1997, St. John’s, Canada, pp. 792-795. Mann, G.K.I., Hu, B.-G., and Gosine, R.G., “Fuzzy PID controller structures”, Proceedings, IEEE 1997 Canadian Conference on Electrical and Computer Engineering, May 25-28, 1997, St. John’s, Canada, pp. 788-791.
  78. Hu, B.-G., Gosine, R.G., and de Silva, C.W., “Classifier design for computer grading systems for food processing”, Proceedings, 1995 IEEE International Conference on Systems, Man and Cybernetics, October 22-25, 1995, Vancouver, BC, pp. 730-735.  

建模与可视化 (Modeling and Applications)

  1. Deng, Y., Tang, F., Dong, W., Yao, H., and B.-G. Hu., "Style-oriented representative paintings selection". In SIGGRAPH Asia 2017 Posters (p. 12). ACM, 2017.
  2. H. Wang, J. Hua, M. Kang, X. Wang, P. de Reffye, and, B.-G. Hu. “Simulating Tree Plasticity with a Functional-structural Plant Model: Being Realistic in Behavior”. In SIMUL 2013, The Fifth International Conference on Advances in System Simulation, pp. 122-129, 2013.

    wanghy13.pdf

  3. N. Wang and B.-G. Hu , “IdiotPencil: An interactive system for generating pencil drawings from 3D polygonal models”, Proceedings of the 12th International Conference on Computer-Aided Design and Computer Graphics, 2011.

    wangning11.pdf

  4. P. de Reffye, E. Heuvelink, Y. Guo, , B.-G. Hu , and B.-G. Zhang, “Coupling Process-Based Models and Plant Architectural Models: A Key Issue for Simulating Crop Production”, in: Crop Modeling and Decision Support, White, JW, Cao, W.,Wang, E.(Eds) , 2009, pp. 130-147.
  5. R. Qi, Y.-T. Ma, B.-G. Hu , P. de Reffye, and P.-H. Cournède, “New Approach for the Study of Source-Sink Dynamics on Maize”, in: Crop Modeling and Decision Support, White, JW, Cao, W.,Wang, E.(Eds) , 2009, pp. 161-168.

    qirui09.pdf

  6. N. Wang and B.-G. Hu , “Aeolian sand movement and interacting with vegetation:a GPU based simulation and visualization method”, in: Plant Growth Modeling and Applications, (PMA09) 2009, pp. 401-408.(DEMO1-83M)(DEMO2-17M)(Talk-PPT) (PDF)
  7. Y. Ma, A. Mathieu, A.M. Wubs, E. Heuvelink, J.Y. Zhu, B.-G. Hu , P.H. Cournede, P.de Reffye, “Parameter estimation and growth variation analysis in six capsicum cultivars with the functional-structural model GreenLab”, in: Plant Growth Modeling and Applications, (PMA09) 2009, pp. 183-190.

    MaYT_pma09.pdf

  8. B.-G. Hu and X.P. Zhang, G. Yang, and M. Jaeger, “Objective Evaluation of 3D Reconstructed Plants and Trees from 2D Image Data”, In CW’08: Proceedings of the international Conference on Cyberworlds, 2008, pp. 263-270.

    HuBG_cw08.pdf

  9. X. Mei, P. Decaudin, B.-G. Hu and X.P. Zhang, “Real-time Marker Level Set on GPU”, In CW’08: Proceedings of the International Conference on Cyberworlds, 2008, pp. 209-216.

    MeiX_cw08.pdf

  10. Zhu C., Zhang X.-P., B.-G. Hu, Jaeger M., “Reconstruction of Tree Crown Shape from Scanned Data”, In: by Pan, Z.; Zhang, X.; El Rhalibi, A.; Woo, W.; Li, Y. (Eds.). Technologies for E-Learning and Digital Entertainment. Third International Conference, Edutainment 2008, Nanjing, China, June 25-27, 2008.

    zhucao08.pdf

  11. Mei, X., Jaeger, M., and B. -G. Hu., “An Effective Stratified Sampling Scheme for Environment Maps with Median Cut Method”, in: Proceedings of International Conference on Computer Graphics, Imaging and Visualisation (CGIV’06), IEEE, pp. 384-389, 2006.

    Meicgiv06.pdf

  12. Teng, J., B.-G. Hu, Jaeger, M., "Fast Tree Ambient Occlusion Approximation", in: Plant Growth Modeling and Applications, (PMA06) 2006, pp. 319-322.

    Teng_pma06.pdf

  13. Wu, L, De Reffye, P., Hu, B.-G., Le Dimet, F.X., "A mathematical model of plant structure dynamics", CARI’06, 1: pp. 1-8.

    WU06.pdf

  14. Wu, L, Le Dimet, F.X., Hu, B.-G., Cournède, P.H., De Reffye, P., "A water supply optimization problem for plant growth based on GreenLab model", CARI’04, ARIMA Journal 3, pp. 194-207.

    wulin05.pdf

  15. W. Yin, M. Jaeger, J. Teng, B.-G. Hu, “Modelling and Sampling Ramified Objects with Substructure-Based Method”, ICCS 2005, LNCS 3515, Springer Verlag, pp. 322-326, 2005.

    YinWW_05.pdf

  16. P. de Reffye, B.-G.Hu, “Relevant Qualitative and Quantitative Choices for Building an Efficient Dynamic Plant Growth Model: GreenLab Case”, in: Plant Growth Modeling and Applications, (PMA03) 2003, pp. 87-107.

    deReffye_Hu_pma03.pdf

  17. B.-G. Hu, Ph. de Reffye, X.Zhao, H.-P.Yan, M.-Z.Kang, “GreenLab: A New Methodology towards Plant Functional-Structual Model-Structual Aspect”, in: Plant Growth Modeling and Applications, (PMA03) 2003, pp. 21-35.

    Hu_pma03.pdf

  18. X. Zhao, P. de Reffye, D. Barthelemy, B.-G. Hu, “Interactive Simulation of Plant Architecture Based on a Dual-Scale Automation Model”, in: Plant Growth Modeling and Applications, (PMA03) 2003, pp. 144-153.

    ZhaoX_pma03.pdf

  19. H.-P. Yan, P. de Reffye, J. Leroux, B.-G. Hu, “Study on Plant Growth Behaviors Simulated by the Functional-Structural Plant Model—GreenLab”, in: Plant Growth Modeling and Applications, 2003, pp. 118-128.

    YanHP_pma03.pdf

  20. Z.-G. Zhan, P. de Reffye, F. Houllier, B.-G Hu, “Fitting a Structural - Functional Model with Plant Architectural Data”, in: Plant Growth Modeling and Applications, (PMA03) 2003, pp. 236-249.

    ZhanZG_pma03.pdf

  21. M.-Z. Kang, P. de Reffye, J.-F. Barczi, B.-G. Hu, F.Houllier, “Stochastic 3D Tree Simulation Using Substructure Instancing”, in: Plant Growth Modeling and Applications, (PMA03) 2003, pp. 154-168.

    KangMZ_pma03.pdf

  22. L. Wu, P. de Reffye, F.-X. Le Dimet, B.-G. Hu, “Optimization of Source-Sinks Relationship Based on a Plant Functional-Structional Model: A Case Study on Maize”, in: Plant Growth Modeling and Applications, (PMA03) 2003, pp. 285-295.

    WuLin_pma03.pdf

  23. Ph. de Reffye, M. Goursat, J.P. Quadrat, B.-G. Hu, “The Dynamic Equations of the Tree Morphogenesis GreenLab Model”, in: Plant Growth Modeling and Applications, (PMA03) 2003, pp. 108-117.

    deReffye_pma03.pdf

  24. Yan, Ph. de Reffye and B.-G. Hu. “Realistic Plant Growth Modelling”, Proceedings of International Conference on Computer Graphics and Spatial Information System, 2002, Beijing, China, pp. 581-585.
  25. H. P. Yan, J. F. Barczi, Ph. de Reffye and B.-G. Hu. “Fast Algorithms of Plant Computation Based on Substructure Instances”. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, 3(10), pp. 145-153, 2002, Plzen - Bory, Czech Republic.
  26. M.Z. Kang, H.P. Yan, Ph. de Reffye, M. Jaeger, B.-G. Hu and F. Houllier, “A Fast Algorithm For Calculating Stem and Branch Radial Growth of A Tree With Substructure Approach”, IUFRO Working Party S5.01-04 Workshop, September 8-15, 2002, Harrison, British Columbia, Canada.
  27. B.-G. Hu,“Material Damping Enhancement towards High-Performance Composite-based Manipulators”, pp. 387-394, Proceedings of Robotic and Knowledge-based Systems Workshop, October 8-15, 1995, St. Hubert, Quebic, Canada.

国内会议论文 (Chinese Conference Papers)

  1. 胡包钢,王泳,“关于互信息学习准则在分类问题中的应用“,2007年全国模式识别学术会议(CCPR2007),2007年12月,北京,科学出版社,2007: 35-45.

    ccpr2007HuBG.pdf

  2. 杨双红,胡包钢,“S-学习:一种模式分类的新方法“,2007年全国模式识别学术会议(CCPR2007),2007年12月,北京,科学出版社,2007: 10-17。 本文获得''CAA最佳论文奖''。

    ccpr2007YangSH.pdf

  3. 王泳,胡包钢,“归一化信息增益准则与准确率、精确率、召回率的非线性关系研究“,2007年全国模式识别学术会议(CCPR2007),2007年12月,北京,科学出版社,2007: 27-34.  

    ccpr2007WangY.pdf

  4. 李昌智,胡包钢,陈翀,“花之舞——基于弹性模型的虚拟植物舞蹈动画系统“,第五届中国计算机图形学大会,2004年9月,西安.  

    LiChangZhi2004.pdf

  5. 黄非, 徐波, 黄泰翼, 胡包钢, "基于领域关键词的词典及语言模型自适应", 全国第五届计算语言学联合学术会议, 1999年, 北京.

    HuangFei.pdf



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