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首页 » 导师库 » 吕宝粮——上海交通大学——仿脑计算机理论与模型、神经网络理论与应用、机器学习、人脸检测与识别、自然语言处理、脑与计算机接口、计算系统生物学

吕宝粮——上海交通大学——仿脑计算机理论与模型、神经网络理论与应用、机器学习、人脸检测与识别、自然语言处理、脑与计算机接口、计算系统生物学

来源:生物谷 2016-07-26 17:37

导师姓名:吕宝粮         导师类别:博士生导师  

  • 姓名: 吕宝粮       性别: 男       出生年月:
  • 所在院校: 上海交通大学       所在院系: 电子信息与电气工程学院
  • 职称: 教授       招生专业: 计算机应用技术
  • 研究领域: 仿脑计算机理论与模型、神经网络理论与应用、机器学习、人脸检测与识别、自然语言处理、脑与计算机接口、计算系统生物学
  • 联系方式

  • E-Mail: blu@cs.sjtu.edu.cn       电话: 021-*******       邮编: 0
  • 地址:
  • 个人简介

    教育及工作经历:
      工学博士、教授、博士生导师、IEEE高级会员。1960年11月生于青岛。1982年1月毕业于青岛科技大学自动化系,获工学学士。同年留校任教。1989年4月毕业于西北工业大学计算机科学与工程系,获工学硕士学位。1991年4月至1994年3月在日本京都大学电气工程系攻读博士学位。主要从事模块化神经网络结构与学习算法和多层神经网络逆映像的计算方法及其应用的研究,提出了多级筛选神经网络模型和基于线性与非线性规划方法的多层神经网络逆映像计算方法。1994年3月获京都大学工学博士学位。1994年4月至1999年3月在日本理化学研究所仿生物控制研究中心任研究员,主要参与日本国家重点研究课题“仿生物控制与自律分散系统”的研究,负责“大规模、复杂模式识别问题的分解与学习”子课题,提出了基于类关系的通用问题分解方法和并列模块化神经网络模型(Min-Max Modular Neural Network)。该模型解决了传统多层前馈网络和反向传播学习算法在解决大规模实际问题时所存在的陷于局部极小值、长时间学习和网络结构设计等问题。该模型已成功地应用于脑电波信号分类、自然语言处理中的自动词性标注、和大规模词汇库的自动纠错等问题。1999年4月至2002年8月在日本理化学研究所脑科学综合研究中心任研究员,主要参与日本国家重点研究课题“创造脑”的研究,负责“仿脑计算机的结构与超并列学习模型”子课题。提出了涌现学习方法、具有局部响应的高斯零交叉判别函数和基于涌现学习方法的仿脑计算机模型。2002年8月起任上海交通大学计算机科学与工程系教授,同年12月被评为博士生导师。
    主讲课程:
      1. 算法设计与数据结构
      2. 神经网络理论与应用
    主要研究领域:
      1. 仿脑计算机理论与模型
      2. 神经网络理论与应用
      3. 机器学习
      4. 人脸检测与识别
      5. 计算系统生物学
      6. 脑-计算机接口
      7. 自然语言处理

    著作及论文

    Book Chapters:
      1) B. L. Lu and K. Ito, ``Transformation of nonlinear programming problems into separable ones using multi-layer neural networks", Mathematics of Neural Networks: Models, Algorithms and Applications, S. W. Ellacott, J. C. Mason, and I. J. Anderson Eds., Kluwer Academic Publishers, pp. 235-239, 1997
    Journal Papers:
      1) B. L. Lu, J. Shin, and M. Ichikawa, “Massively parallel classification of single-trial EEG signals using a min-max modular neural network”, IEEE Trans. Biomedical Engineering, vol. 51, no. 3, pp. 551-558, 2004
      2) B. L. Lu and K. Ito, “Converting general nonlinear programming problems into separable programming problems with feedforward neural networks”, Neural Networks, vol. 16, pp. 1059-1074, 2003
      3) B. L. Lu, Q. Ma, M. Ichikawa, and H. Isahara, “Efficient part-of-speech tagging with a min-max modular neural network”, Applied Intelligence, vol 19, pp. 65-81, 2003
      4) Q. Ma, B. L. Lu, H. Isahara, and M. Ichikawa, ``Part of speech tagging with min-max modular neural networks”, Systems and Computers in Japan, vol. 33, no. 7, 2002
      5) B. L. Lu, J. Shin, and M. Ichikawa, “Effects of features on generalization accuracy of min-max modular neural networks in the classification of single-trial EEG signals”, RIKEN Review, vol. 48, 2002
      6) J. Shin, B. L. Lu, A. Talnov, G. Matsumoto, and J. Brankack, “Reading auditory discrimination behaviour of freely moving rats from hippocampal EEG”, Neurocomputing, vol 38-40, pp. 1557-1566, 2001
      7) B. L. Lu, J. Shin, and M. Ichikawa, “Fast classification of high-dimensional EEG signals using min-max modular neural networks”, RIKEN Review, vol. 40, pp. 58-62, 2001
      8) Q. Ma, B. L. Lu, H. Isahara, and M. Ichikawa, “part of speech tagging with min-max modular neural networks” (in Japanese), IEICE Transactions on Information and Systems, D-II, vol. J84-D-II, no. 4, pp. 708-717, 2001
      9) B. L. Lu, Q. Ma, M. Ichikawa, and H. Isahara, “Massively parallel learning of part-of-speech disambiguation”, RIKEN Review, no. 30, pp. 40-49, 2000
    International Conference Papers:
      1. B. L. Lu, K. A. Wang, and Y. M. Wen, “Comparison of parallel and cascade methods for training support vector machines on large-scale problems” (invited paper), Proc. of International Conference on Machine Learning and Cybernetics ( ICMLC04), pp. 3056-3061, Shanghai, China, Aug. 26-29, 2004
      2. Y. M. Wen and B. L. Lu, “A cascade method for reducing training time and the number of support vectors”, Advances in Neural Networks-ISNN2004, Lecture Notes in Computer Science, vol. 3173, part I, pp. 480-485, 2004
      3. Z. G. Fan and B. L. Lu, “An adjusted gaussian skin-color model based on principle component analysis”, Advances in Neural Networks-ISNN2004, Lecture Notes in Computer Science, vol. 3173, part I, pp. 804-809, 2004
      4. H. Zhao and B. L. Lu, “Analysis of fault tolerance of a combining classifier”, Advances in Neural Networks-ISNN2004, Lecture Notes in Computer Science, vol. 3173, part I, pp. 888-893, 2004
      5. B. L. Lu, K. A. Wang, M. Utiyama, and H. Isahara, “A part-versus-part method for massively parallel training of support vector machines”, Proc. of IEEE/INNS Int. Joint Conf. on Neural Networks ( IJCNN2004), pp.735-740, Budabest, Hungary, July 25-29, 2004
      6. B. L. Lu, “A massively parallel machine learning approach to text categorization”, The 3rd Japan-China Natural Language Processing Joint Research Promotion Conference (invited paper), Shiga, Japan, Nov. 11, 2003
      7. B. L. Lu and M. Ichikawa, “Emergent on-line learning with a Gaussian zero-crossing discriminant function”, Proc. of IEEE/INNS Int. Joint Conf. on Neural Networks, Honolulu, USA, pp. 1263-1268, 2002
      8. B. L. Lu and M. Ichikawa, “Emergent on-line learning in min-max modular neural networks”, Proc. of IEEE/INNS Int. Conf. on Neural Networks, Washington DC, USA, pp. 2650-2655, 2001
      9. B. L. Lu, J. Shin, and M. Ichikawa, “Massively parallel classification of EEG signals using min-max modular neural networks”, Lecture Notes in Computer Science, vol. 2130, pp. 601-608, 2001
      10. Q. Ma, B. L. Lu, M. Murata, H. Isahara, and M. Ichikawa, “On-line error detection of annotated corpus using modular neural networks”, Lecture Notes in Computer Science, vol. 2130, pp. 1185-1192, 2001
      11. B. L. Lu and M. Ichikawa, “Emergent on-line learning: towards brain-style computer”, Proc. of Int. Conf. on Neural Information Processing , Shanghai, China, vol. 2, pp. 655-658, 2001
      12. Q. Ma and B. L. Lu, “Emergent learning and natural language processing”, Proc. of Int. Conf. on Neural Information Processing, Shanghai, China, vol. 2, pp. 659-664, 2001
      13. B. L. Lu and M. Ichikawa, “A Gaussian zero-crossing discriminat function for min-max modular neural networks”, in Proc. of 5th International Conference on Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies (KES’01), pp. 298-302, Osaka, Japan, 6-8 September, 2000
      14. B. L. Lu and M. Ichikawa, “Emergence of learning: an approach to coping with NP-complete problems in learning”, Proc. of IEEE-INNS-ENNS Int. Joint Conf. On Neural Networks, Como, Italy, vol. 4, pp. 159-164, 2000
    Technical Reports and Oral Presentation:
      1) 吕宝粮 王逸飞. 一种基于涌现理论的增量学习模型. 第十三届全国神经网络学术大会论文集. pp. 213-218, 人民邮电出版社, 2003
      2) B. L. Lu and M. Ichikawa, “An Emergent learning method capable of training a class of pattern classifiers in polynomial time and space”, Technical Report of IEICE, July 26, 2002
      3) B. L. Lu and M. Ichikawa, “Emergent learning; from linear threshold gates to universal classifiers”, Int. Workshop on Information Processing on the Brain 2000, Hangzhu, China, 2000
      4) B. L. Lu and M. Ichikawa, “Coping with NP-complete problems in learning of modular neural networks”, Technical Report of IEICE, NC99-150-181, pp. 173-179, 2000
      5) B. L. Lu and M. Ichikawa, “An upper bound on the size of min-max modular neural networks trainable in polynomial time”, Technical Report of IEICE, NLP2000-15-29, pp. 55-62, 2000
      6) Q. Ma, B. L. Lu, H. Isahara, and M. Ichikawa, ``Disambiguation of part-of-speech with modular neural networks" (in Japanese), Technical Report of IEICE, NC-2000-17, pp. 63-70, 2000

    承担项目

      1. 国家自然科学基金项目:增量学习模型研究;2004.1-2006.12 (项目编号:60375022)
      2. 国家自然科学基金项目:超并列模式分类器的问题分解与模块集成研究;2005.1-2007.12 (项目编号:60473040)
      3. 中日合作研究项目:基于增量学习模型的图像错误诊断系统;2004.6-2005.3
      4. 中日合作研究项目:基于超并列机器学习方法的大规模文本分类研究;2004.4-2005.3
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