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Author:
Ryohei Nakano: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Ryohei Nakano
    Translation with Optimization from Relational Calculus to Relational Algebra Having Aggregate Functions. [Citation Graph (6, 18)][DBLP]
    ACM Trans. Database Syst., 1990, v:15, n:4, pp:518-557 [Journal]
  2. Ryohei Nakano
    Integrity Checking in a Logic-Oriented ER Model. [Citation Graph (2, 13)][DBLP]
    ER, 1983, pp:551-564 [Conf]
  3. Ryohei Nakano, Minoru Kiyama
    MACH: Much Faster Associative Machine. [Citation Graph (2, 0)][DBLP]
    IWDM, 1987, pp:339-352 [Conf]
  4. Ryohei Nakano, Kazumi Saito
    Computational Characteristics of Law Discovery Using Neural Networks. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1998, pp:342-351 [Conf]
  5. Ryohei Nakano, Kazumi Saito
    Discovery of a Set of Nominally Conditioned Polynomials. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1999, pp:287-298 [Conf]
  6. Kazumi Saito, Ryohei Nakano
    Discovery of Nominally Conditioned Polynomials Using Neural Networks, Vector Quantizers and Decision Trees. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2000, pp:325-329 [Conf]
  7. Kazumi Saito, Ryohei Nakano
    Structuring Neural Networks through Bidirectional Clustering of Weights. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:206-219 [Conf]
  8. Ryohei Nakano, Kazumi Saito
    Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. [Citation Graph (0, 0)][DBLP]
    Progress in Discovery Science, 2002, pp:482-493 [Conf]
  9. Ken-ichi Arai, Ryohei Nakano
    Annealed RNN Learning of Finite State Automata. [Citation Graph (0, 0)][DBLP]
    ICANN, 1996, pp:519-524 [Conf]
  10. Masahiro Kimura, Ryohei Nakano
    Learning Dynamical Systems Produced by Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 1996, pp:133-138 [Conf]
  11. Masahiro Kimura, Ryohei Nakano
    Unique Representations of Dynamical Systems Produced by Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:403-408 [Conf]
  12. Yuval Davidor, Takeshi Yamada, Ryohei Nakano
    The ECOlogical Framework II : Improving GA Performance At Virtually Zero Cost. [Citation Graph (0, 0)][DBLP]
    ICGA, 1993, pp:171-176 [Conf]
  13. Ryohei Nakano, Takeshi Yamada
    Conventional Genetic Algorithm for Job Shop Problems. [Citation Graph (0, 0)][DBLP]
    ICGA, 1991, pp:474-479 [Conf]
  14. Ryohei Nakano, Kazumi Saito
    Finding Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. [Citation Graph (0, 0)][DBLP]
    IDA, 2001, pp:258-267 [Conf]
  15. Kazumi Saito, Ryohei Nakano
    Adaptive Concept Learning Algorithm. [Citation Graph (0, 0)][DBLP]
    IFIP Congress (1), 1994, pp:294-299 [Conf]
  16. Kazumi Saito, Ryohei Nakano
    Law Discovery using Neural Networks. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1997, pp:1078-1083 [Conf]
  17. Daisuke Kitakoshi, Hiroyuki Shioya, Ryohei Nakano
    Analysis for Adaptability of Policy-Improving System with a Mixture Model of Bayesian Networks to Dynamic Environments. [Citation Graph (0, 0)][DBLP]
    KES (4), 2005, pp:730-737 [Conf]
  18. Kazumi Saito, Ryohei Nakano
    Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach. [Citation Graph (0, 0)][DBLP]
    KES (2), 2006, pp:945-952 [Conf]
  19. Yusuke Tanahashi, Kazumi Saito, Daisuke Kitakoshi, Ryohei Nakano
    Finding Nominally Conditioned Multivariate Polynomials Using a Four-Layer Perceptron Having Shared Weights. [Citation Graph (0, 0)][DBLP]
    KES (2), 2006, pp:969-976 [Conf]
  20. Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano
    Piecewise Multivariate Polynomials Using a Four-Layer Perceptron. [Citation Graph (0, 0)][DBLP]
    KES, 2004, pp:602-608 [Conf]
  21. Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano
    Model Selection and Weight Sharing of Multi-layer Perceptrons. [Citation Graph (0, 0)][DBLP]
    KES (4), 2005, pp:716-722 [Conf]
  22. Satoshi Tanimoto, Ryohei Nakano
    Learning an Evaluation Function for Shogi from Data of Games. [Citation Graph (0, 0)][DBLP]
    KES, 2004, pp:609-615 [Conf]
  23. Kazumi Saito, Ryohei Nakano
    A concept learning algorithm with adaptive search. [Citation Graph (0, 0)][DBLP]
    Machine Intelligence 14, 1993, pp:353-0 [Conf]
  24. Kazumi Saito, Ryohei Nakano
    A Connectionist Approach to Numeric Law Discorvery. [Citation Graph (0, 0)][DBLP]
    Machine Intelligence 15, 1995, pp:315-327 [Conf]
  25. Kazumi Saito, Ryohei Nakano
    Second-order Learning Algorithm with Squared Penalty Term. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:627-633 [Conf]
  26. Naonori Ueda, Ryohei Nakano
    Deterministic Annealing Variant of the EM Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:545-552 [Conf]
  27. Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton
    SMEM Algorithm for Mixture Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:599-605 [Conf]
  28. Kazumi Saito, Ryohei Nakano
    Discovery of Relevant Weights by Minimizing Cross-Validation Error. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:372-375 [Conf]
  29. Ryohei Nakano, Yuval Davidor, Takeshi Yamada
    Optimal Population Size under Constant Computation Cost. [Citation Graph (0, 0)][DBLP]
    PPSN, 1994, pp:130-138 [Conf]
  30. Takeshi Yamada, Ryohei Nakano
    A Genetic Algorithm Applicable to Large-Scale Job-Shop Problems. [Citation Graph (0, 0)][DBLP]
    PPSN, 1992, pp:283-292 [Conf]
  31. Takeshi Yamada, Ryohei Nakano
    Scheduling by Genetic Local Search with Multi-Step Crossover. [Citation Graph (0, 0)][DBLP]
    PPSN, 1996, pp:960-969 [Conf]
  32. Takeshi Yamada, Kazuyuki Yoshimura, Ryohei Nakano
    Information Operator Scheduling by Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    SEAL, 1998, pp:50-57 [Conf]
  33. Kazumi Saito, Ryohei Nakano
    Second-Order Learning Algorithm with Squared Penalty Term. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:3, pp:709-729 [Journal]
  34. Kazumi Saito, Ryohei Nakano
    Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1997, v:9, n:1, pp:123-141 [Journal]
  35. Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton
    SMEM Algorithm for Mixture Models. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:9, pp:2109-2128 [Journal]
  36. Ken-ichi Arai, Ryohei Nakano
    Stable behavior in a recurrent neural network for a finite state machine. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2000, v:13, n:6, pp:667-680 [Journal]
  37. Masahiro Kimura, Ryohei Nakano
    Learning dynamical systems by recurrent neural networks from orbits. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1998, v:11, n:9, pp:1589-1599 [Journal]
  38. Kazumi Saito, Ryohei Nakano
    Extracting regression rules from neural networks. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2002, v:15, n:10, pp:1279-1288 [Journal]
  39. Naonori Ueda, Ryohei Nakano
    A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1994, v:7, n:8, pp:1211-1227 [Journal]
  40. Naonori Ueda, Ryohei Nakano
    Deterministic annealing EM algorithm. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1998, v:11, n:2, pp:271-282 [Journal]
  41. Masahiro Kimura, Ryohei Nakano
    Dynamical systems produced by recurrent neural networks. [Citation Graph (0, 0)][DBLP]
    Systems and Computers in Japan, 2000, v:31, n:4, pp:77-86 [Journal]
  42. Naonori Ueda, Ryohei Nakano
    EM algorithm with split and merge operations for mixture models. [Citation Graph (0, 0)][DBLP]
    Systems and Computers in Japan, 2000, v:31, n:5, pp:1-11 [Journal]
  43. Masahiro Kimura, Kazumi Saito, Ryohei Nakano
    Extracting Influential Nodes for Information Diffusion on a Social Network. [Citation Graph (0, 0)][DBLP]
    AAAI, 2007, pp:1371-1376 [Conf]
  44. Kosuke Inagaki, Ryohei Nakano
    Learning Evaluation Functions of Shogi Positions from Different Sets of Games. [Citation Graph (0, 0)][DBLP]
    KES (3), 2007, pp:210-217 [Conf]
  45. Yusuke Tanahashi, Daisuke Kitakoshi, Ryohei Nakano
    Nominally Piecewise Multiple Regression Using a Four-Layer Perceptron. [Citation Graph (0, 0)][DBLP]
    KES (3), 2007, pp:218-226 [Conf]
  46. Kazumi Saito, Ryohei Nakano, Masahiro Kimura
    Prediction of Link Attachments by Estimating Probabilities of Information Propagation. [Citation Graph (0, 0)][DBLP]
    KES (3), 2007, pp:235-242 [Conf]

  47. Bidirectional Clustering of MLP Weights for Finding Nominally Conditioned Polynomials. [Citation Graph (, )][DBLP]


  48. Nominally Conditioned Linear Regression. [Citation Graph (, )][DBLP]


  49. A Bayesian Graph Clustering Approach Using the Prior Based on Degree Distribution. [Citation Graph (, )][DBLP]


  50. Variational Bayes from the Primitive Initial Point for Gaussian Mixture Estimation. [Citation Graph (, )][DBLP]


  51. Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy. [Citation Graph (, )][DBLP]


  52. Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR. [Citation Graph (, )][DBLP]


  53. Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error. [Citation Graph (, )][DBLP]


  54. Landscape of a Likelihood Surface for a Gaussian Mixture and its use for the EM Algorithm. [Citation Graph (, )][DBLP]


  55. Optimizing Sparse Kernel Ridge Regression hyperparameters based on leave-one-out cross-validation. [Citation Graph (, )][DBLP]


  56. A Set of Linear Regressions with Automatic Nominal Space Partition Using a Four-Layer Perceptron. [Citation Graph (, )][DBLP]


  57. Reducing SVR Support Vectors by Using Backward Deletion. [Citation Graph (, )][DBLP]


  58. Prediction of Information Diffusion Probabilities for Independent Cascade Model. [Citation Graph (, )][DBLP]


  59. EM Algorithm with PIP Initialization and Temperature-Based Selection. [Citation Graph (, )][DBLP]


  60. Extracting influential nodes on a social network for information diffusion. [Citation Graph (, )][DBLP]


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