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Masumi Ishikawa: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Masumi Ishikawa
    Neural Networks Approach to Rule Extraction. [Citation Graph (0, 0)][DBLP]
    ANNES, 1995, pp:6-9 [Conf]
  2. Wlodzislaw Duch, Rafal Adamczak, Krzysztof Grabczewski, Masumi Ishikawa, Hiroki Ueda
    Extraction of crisp logical rules using constrained backpropagation networks. [Citation Graph (0, 0)][DBLP]
    ESANN, 1997, pp:- [Conf]
  3. Masumi Ishikawa, Kazuhiko Nishino
    Designing Neural Networks by a Combination of Structural Learning and Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:415-420 [Conf]
  4. Kikuo Fujimura, Heizo Tokutaka, Masumi Ishikawa
    Modified Counter-Propagation Network. [Citation Graph (0, 0)][DBLP]
    ICONIP, 1998, pp:550-553 [Conf]
  5. Masumi Ishikawa, Shinya Kawashima, Naoto Homma
    Memory-Based Location Estimation and Navigation Using Bayesian Estimation. [Citation Graph (0, 0)][DBLP]
    ICONIP, 1998, pp:112-117 [Conf]
  6. Keiji Kamei, Masumi Ishikawa
    A Genetic Approach to Optimizing the Values of Parameters in Reinforcement Learning for Navigation of a Mobile Robot. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:1148-1153 [Conf]
  7. Xiang Yu, Masumi Ishikawa, Huisheng Chi
    Rule Extraction from Data by Combining a Structural Learning with Forgetting and Linear Discriminant Functions. [Citation Graph (0, 0)][DBLP]
    ICONIP, 1998, pp:1626-1629 [Conf]
  8. Alireza Ahrary, Li Tian, Sei-ichiro Kamata, Masumi Ishikawa
    An Autonomous Sewer Robots Navigation Based on Stereo Camera Information. [Citation Graph (0, 0)][DBLP]
    ICTAI, 2005, pp:628-633 [Conf]
  9. Masumi Ishikawa, Hirohito Shimada, Shun-ichi Amari
    Iterative Design of Regularizers Based on Data by Minimizing Generalization Errors. [Citation Graph (0, 0)][DBLP]
    IJCNN (1), 2000, pp:3-8 [Conf]
  10. Hong Zhang, Masumi Ishikawa
    Performance Improvement of Hybrid Real-Coded Genetic Algorithm with Local Search and Its Applications. [Citation Graph (0, 0)][DBLP]
    CIMCA/IAWTIC, 2005, pp:1171-1176 [Conf]
  11. Masumi Ishikawa
    Learning of Modular Structured Networks. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1995, v:75, n:1, pp:51-62 [Journal]
  12. Masumi Ishikawa
    Rule Extraction by Structural Learning with an Immediate Critic. [Citation Graph (0, 0)][DBLP]
    JACIII, 1999, v:3, n:5, pp:341-347 [Journal]
  13. Masumi Ishikawa
    Rule extraction by successive regularization. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2000, v:13, n:10, pp:1171-1183 [Journal]
  14. Masumi Ishikawa
    Structural learning with forgetting. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1996, v:9, n:3, pp:509-521 [Journal]
  15. Masumi Ishikawa, Risto Miikkulainen, Helge Ritter
    New developments in self-organizing systems. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2004, v:17, n:8-9, pp:1037- [Journal]
  16. Fredrik Linåker, Masumi Ishikawa
    Real-time appearance-based Monte Carlo localization. [Citation Graph (0, 0)][DBLP]
    Robotics and Autonomous Systems, 2006, v:54, n:3, pp:205-220 [Journal]

  17. Brain-Inspired Emergence of Behaviors in Mobile Robots by Reinforcement Learning with Internal Rewards. [Citation Graph (, )][DBLP]


  18. Evolutionary Canonical Particle Swarm Optimizer - A Proposal of Meta-optimization in Model Selection. [Citation Graph (, )][DBLP]


  19. Hierarchical Architecture with Modular Network SOM and Modular Reinforcement Learning. [Citation Graph (, )][DBLP]


  20. Task Segmentation in a Mobile Robot by mnSOM and Clustering with Spatio-temporal Contiguity. [Citation Graph (, )][DBLP]


  21. Brain-Inspired Emergence of Behaviors Based on the Desire for Existence by Reinforcement Learning. [Citation Graph (, )][DBLP]


  22. Surprise-Driven Exploration with Rao-Blackwellized Particle Filters for Efficiently Constructing Occupancy Grid Maps. [Citation Graph (, )][DBLP]


  23. A New Approach to Task Segmentation in Mobile Robots by mnSOM. [Citation Graph (, )][DBLP]


  24. An Evolutionary Approach to Data Classification - Hybrid Real-Coded Genetic Algorithm with Pruning. [Citation Graph (, )][DBLP]


  25. Formation of graph-based maps for mobile robots using Hidden Markov Models. [Citation Graph (, )][DBLP]


  26. Evolutionary Particle Swarm Optimization (EPSO) - Estimation of Optimal PSO Parameters by GA. [Citation Graph (, )][DBLP]


  27. Experimental evaluation of intelligent fault detection system for inspection of sewer pipes. [Citation Graph (, )][DBLP]


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