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本系簡介
水谷英二 助理教授

image 個人資料

                   水谷英二

image 水谷英二 | Eiji Mizutani

 

  • 最高學歷:國立清華大學資訊工程博士 (Ph.D in Computer Science)
  • 教授課程:動態規劃、排程、馬可夫決策過程、機率模型、非線性最佳化
  • 研究領域:動態規劃、數值線性代數、非線性最佳化、機器學習法
  • 實驗室:非線性數值最佳化實驗室(MA011-1) 分機:7937

image 各位新進學生:

我這邊有一個國科會資助國內研究生的研究機會, 內容是與斯圖爾特德雷福斯教授(加州大學柏克萊分校之名譽教授)共同研究的一個小型計畫. 在研究所學習期間, 可有柏克萊大學之旅的補助. 如果你有興趣請用電子郵件並用英文書寫來跟我聯絡.

image Selected recent papers (已挑選)

  1. Eiji Mizutani and Stuart Dreyfus."Totally model-free actor-critic recurrent neural-network reinforcement learning in non-Markovian domains." Annals of Operations Research(SCI) ,pp 1-25,2016. DOI: 10.1007/s10479-016-2366-2
  2. Eiji Mizutani. "On Pantoja's problem allegedly showing a distinction between differential dynamic programming and stagewise Newton methods." International Journal of Control, Taylor & Francis (SCI), vol.8, issue 9, pp.1702-1711, 2015.
  3. Eiji Mizutani. "A counterexample to a proposed dynamic programming algorithm for optimal bid construction in an auction-based fully distributed manufacturing system." International Journal of Advanced Manufacturing Technology, Springer (SCI), Vol.71, pp.377-380, 2014.
  4. Eiji Mizutani. "A note on dynamic programming formulations for scheduling job classes with changeover times on a single machine." In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, (Received an Outstanding Paper Award), five pages, (EI), 2013.
  5. Eiji Mizutani. "Reformulation of Lawler's algorithm by auxiliary-information dynamic programming in a minimax-cost scheduling problem." In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp.192-196, (EI), 2012.
  6. Eiji Mizutani and James Demmel. "On improving trust-region variable projection algorithms for separable nonlinear least squares learning." In Proceedings of IJCNN 2011 (EI), pp.397-404.
  7. Eiji Mizutani and Stuart Dreyfus. "An analysis on negative curvature induced by singularity in multi-layer neural-network learning." Advances in Neural Information Processing Systems, Vol. 23, pages1669-1677, 2010.
  8. Eiji Mizutani and Stuart Dreyfus. "Stage-lookahead dynamic programming algorithms for stochastic problems with time-lagged control dynamics." In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp.301-305 (EI), 2009.
  9. Eiji Mizutani and Stuart Dreyfus. "Second-order stagewise backpropagation for Hessian-matrix analyses and investigation of negative curvature." Journal of Neural Networks (SCI), Elsevier Science, vol.21, pp.193-203,2008 (Errata).
  10. Eiji Mizutani and Jing-Yun Carey Fan. "On exploiting symmetry for multilayer perceptron learning." In Proceedings of IJCNN, pp.2857-2862, 2007.
  11. Eiji Mizutani and Stuart E. Dreyfus. "On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning." In Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Vancouver, CANADA, July 2006.
  12. Eiji Mizutani and Stuart E. Dreyfus. "Stagewise Newton, differential dynamic programming, and neighboring optimum control for neural-network learning." In Proceedings of the American Control Conference (ACC'05), pp. 1331-1336, Portland Oregon, June 8-10, 2005.
  13. Eiji Mizutani, Stuart E. Dreyfus, and James W. Demmel. "Second-order backpropagation algorithms for a stagewise-partitioned separable Hessian matrix." In Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Montreal CANADA, August 2005.
  14. Eiji Mizutani and James W. Demmel. "Iterative scaled trust-region learning in Krylov subspaces via Pearlmutter's implicit sparse Hessian-vector multiply." Advances in Neural Information Processing Systems, Vol. 16, pages 209-216, edited by S. Thrun, L. Saul, and B. Scholkopf, MIT Press, 2004.
  15. Eiji Mizutani and Stuart E. Dreyfus. "On using discretized Cohen-Grossberg node dynamics for model-free actor-critic neural learning in non-Markovian domains." In Proceedings of the 5th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA2003), Vol.1, pages 1-6, Kobe JAPAN, July 16-20, 2003.
  16. Eiji Mizutani and James W. Demmel. "On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning." Journal of Neural Networks (SCI), Elsevier Science, Vol. 16, pp. 745-753, 2003.
  17. Eiji Mizutani and Kenichi Nishio. "Multi-Illuminant Color Reproduction for Electronic Cameras Via CANFIS Neuro-Fuzzy Modular Network Device Characterization." IEEE Transactions on Neural Networks (SCI), Vol. 13, No. 4, pages 1009-1022, July 2002.
  18. Eiji Mizutani and James W. Demmel. "On separable nonlinear least squares algorithms for neuro-fuzzy modular network learning." In Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Vol.3, pp. 2399-2404, Honolulu USA, May 2002.
  19. Eiji Mizutani and James W. Demmel. "On Iterative Krylov-Dogleg Trust-Region Steps for Solving Neural Networks Nonlinear Least Squares Problems." Advances in Neural Information Processing Systems, vol.13, pp. 605-611, MIT Press, 2001.
  20. Eiji Mizutani, Stuart Dreyfus, and Ken-ichi Nishio. "On derivation of MLP backpropagation from the Kelley-Bryson optimal-control gradient formula and its application." In Proceedings of the IEEE-INNS International Joint Conference on Neural Networks, pp. 2167-2172 (vol.2), Como, Italy, July, 2000.
  21. Eiji Mizutani. "Sample path-based policy-only learning by actor neural networks." In Proceedings of the IEEE International Conference on Neural Networks, pp. 1245-1250 (vol.2) , 1999.

image 其他著作 (Book)

  1. J.-S. R. Jang, C.-T. Sun, and E. Mizutani. Neuro-Fuzzy and Soft Computing: a computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, NJ, 1997 (614 pages).

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image 國際專利

  1. Sony Corporation: Ken-ichi Nishio and Eiji Mizutani. "Methods and apparatus for color device characterization." PCT WO 0065841.
  2. Matsushita Electric Co., Kansai Paint Co., Ltd, and University of California, Berkeley (Takagi, Mizutani, and Auslander). "Proportion predicting systems and methods of making mixture." US 6081796A.

更新日期:2016/12/01