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Kazumi Saito: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Akinori Fujino, Naonori Ueda, Kazumi Saito
    A Hybrid Generative/Discriminative Approach to Semi-Supervised Classifier Design. [Citation Graph (0, 0)][DBLP]
    AAAI, 2005, pp:764-769 [Conf]
  2. Akinori Fujino, Naonori Ueda, Kazumi Saito
    A Classifier Design Based on Combining Multiple Components by Maximum Entropy Principle. [Citation Graph (0, 0)][DBLP]
    AIRS, 2005, pp:423-438 [Conf]
  3. Dileep George, Kazumi Saito, Pat Langley, Stephen D. Bay, Kevin R. Arrigo
    Discovering Ecosystem Models from Time-Series Data. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2003, pp:141-152 [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, Stephen D. Bay, Pat Langley
    Revising Qualitative Models of Gene Regulation. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:59-70 [Conf]
  7. Kazumi Saito, Dileep George, Stephen D. Bay, Jeff Shrager
    Inducing Biological Models from Temporal Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2003, pp:468-469 [Conf]
  8. Kazumi Saito, Pat Langley, Trond Grenager, Christopher Potter, Alicia Torregrosa, Steven A. Klooster
    Computational Revision of Quantitative Scientific Models. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2001, pp:336-349 [Conf]
  9. 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]
  10. Kazumi Saito, Ryohei Nakano
    Structuring Neural Networks through Bidirectional Clustering of Weights. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:206-219 [Conf]
  11. 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]
  12. Masahiro Kimura, Kazumi Saito, Naonori Ueda
    Modeling of growing networks with directional attachment and communities. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:15-20 [Conf]
  13. Tomoharu Iwata, Kazumi Saito, Naonori Ueda
    Visual nonlinear discriminant analysis for classifier design. [Citation Graph (0, 0)][DBLP]
    ESANN, 2006, pp:283-288 [Conf]
  14. Kazumi Saito, Takeshi Yamada
    Extracting Communities from Complex Networks by the k-dense Method. [Citation Graph (0, 0)][DBLP]
    ICDM Workshops, 2006, pp:300-304 [Conf]
  15. Pat Langley, Dileep George, Stephen D. Bay, Kazumi Saito
    Robust Induction of Process Models from Time-Series Data. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:432-439 [Conf]
  16. Takeshi Yamada, Kazumi Saito, Naonori Ueda
    Cross-Entropy Directed Embedding of Network Data. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:832-839 [Conf]
  17. 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]
  18. Kazumi Saito, Ryohei Nakano
    Adaptive Concept Learning Algorithm. [Citation Graph (0, 0)][DBLP]
    IFIP Congress (1), 1994, pp:294-299 [Conf]
  19. Kazumi Saito, Ryohei Nakano
    Law Discovery using Neural Networks. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1997, pp:1078-1083 [Conf]
  20. Akinori Fujino, Naonori Ueda, Kazumi Saito
    Semi-Supervised Learning for Multi-Component Data Classification. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:2754-2759 [Conf]
  21. Tomoharu Iwata, Kazumi Saito, Takeshi Yamada
    Recommendation method for extending subscription periods. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:574-579 [Conf]
  22. Naonori Ueda, Kazumi Saito
    Single-shot detection of multiple categories of text using parametric mixture models. [Citation Graph (0, 0)][DBLP]
    KDD, 2002, pp:626-631 [Conf]
  23. Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao
    Visualizing Dynamics of the Hot Topics Using Sequence-Based Self-organizing Maps. [Citation Graph (0, 0)][DBLP]
    KES (4), 2005, pp:745-751 [Conf]
  24. Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao
    Visualization Architecture Based on SOM for Two-Class Sequential Data. [Citation Graph (0, 0)][DBLP]
    KES (2), 2006, pp:929-936 [Conf]
  25. Tomoharu Iwata, Kazumi Saito
    Visualisation of Anomaly Using Mixture Model. [Citation Graph (0, 0)][DBLP]
    KES, 2004, pp:624-631 [Conf]
  26. Yuji Kaneda, Naonori Ueda, Kazumi Saito
    Extended Parametric Mixture Model for Robust Multi-labeled Text Categorization. [Citation Graph (0, 0)][DBLP]
    KES, 2004, pp:616-623 [Conf]
  27. Masahiro Kimura, Kazumi Saito
    Approximate Solutions for the Influence Maximization Problem in a Social Network. [Citation Graph (0, 0)][DBLP]
    KES (2), 2006, pp:937-944 [Conf]
  28. Masahiro Kimura, Kazumi Saito, Kazuhiro Kazama, Shin-ya Sato
    Detecting Search Engine Spam from a Trackback Network in Blogspace. [Citation Graph (0, 0)][DBLP]
    KES (4), 2005, pp:723-729 [Conf]
  29. 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]
  30. 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]
  31. 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]
  32. 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]
  33. Kazumi Saito, Ryohei Nakano
    A concept learning algorithm with adaptive search. [Citation Graph (0, 0)][DBLP]
    Machine Intelligence 14, 1993, pp:353-0 [Conf]
  34. Kazumi Saito, Ryohei Nakano
    A Connectionist Approach to Numeric Law Discorvery. [Citation Graph (0, 0)][DBLP]
    Machine Intelligence 15, 1995, pp:315-327 [Conf]
  35. Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum
    Parametric Embedding for Class Visualization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  36. Kazumi Saito, Ryohei Nakano
    Second-order Learning Algorithm with Squared Penalty Term. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:627-633 [Conf]
  37. Naonori Ueda, Kazumi Saito
    Parametric Mixture Models for Multi-Labeled Text. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:721-728 [Conf]
  38. Kazumi Saito, Ryohei Nakano
    Discovery of Relevant Weights by Minimizing Cross-Validation Error. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:372-375 [Conf]
  39. Masahiro Kimura, Kazumi Saito
    Tractable Models for Information Diffusion in Social Networks. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:259-271 [Conf]
  40. Kazumi Saito, Pat Langley
    Discovering Empirical Laws of Web Dynamics. [Citation Graph (0, 0)][DBLP]
    SAINT, 2002, pp:168-175 [Conf]
  41. Akinori Fujino, Naonori Ueda, Kazumi Saito
    A hybrid generative/discriminative approach to text classification with additional information. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Manage., 2007, v:43, n:2, pp:379-392 [Journal]
  42. 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]
  43. 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]
  44. Pablo A. Estévez, Cristián J. Figueroa, Kazumi Saito
    Cross-entropy embedding of high-dimensional data using the neural gas model. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:5-6, pp:727-737 [Journal]
  45. Masahiro Kimura, Kazumi Saito, Naonori Ueda
    Modeling of growing networks with directional attachment and communities. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2004, v:17, n:7, pp:975-988 [Journal]
  46. 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]
  47. Masahiro Kimura, Kazumi Saito, Naonori Ueda
    Modeling network growth with directional attachment and communities. [Citation Graph (0, 0)][DBLP]
    Systems and Computers in Japan, 2004, v:35, n:8, pp:1-11 [Journal]
  48. Naonori Ueda, Kazumi Saito
    Parametric mixture model for multitopic text. [Citation Graph (0, 0)][DBLP]
    Systems and Computers in Japan, 2006, v:37, n:2, pp:56-66 [Journal]
  49. 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]
  50. Kazumi Saito, Pat Langley
    Quantitative Revision of Scientific Models. [Citation Graph (0, 0)][DBLP]
    Computational Discovery of Scientific Knowledge, 2007, pp:120-137 [Conf]
  51. Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao
    Interpretable Likelihood for Vector Representable Topic. [Citation Graph (0, 0)][DBLP]
    KES (3), 2007, pp:202-209 [Conf]
  52. Manabu Kimura, Kazumi Saito, Naonori Ueda
    Pivot Learning for Efficient Similarity Search. [Citation Graph (0, 0)][DBLP]
    KES (3), 2007, pp:227-234 [Conf]
  53. 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]
  54. Tomoharu Iwata, Kazumi Saito, Takeshi Yamada
    Modeling user behavior in recommender systems based on maximum entropy. [Citation Graph (0, 0)][DBLP]
    WWW, 2007, pp:1281-1282 [Conf]

  55. Minimizing the Spread of Contamination by Blocking Links in a Network. [Citation Graph (, )][DBLP]


  56. Learning to Predict Opinion Share in Social Networks. [Citation Graph (, )][DBLP]


  57. Discovering Influential Nodes for SIS Models in Social Networks. [Citation Graph (, )][DBLP]


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


  59. Combining Burst Extraction Method and Sequence-Based SOM for Evaluation of Fracture Dynamics in Solid Oxide Fuel Cell. [Citation Graph (, )][DBLP]


  60. Efficient Estimation of Influence Functions for SIS Model on Social Networks. [Citation Graph (, )][DBLP]


  61. Selecting the Most Influential Nodes in Social Networks. [Citation Graph (, )][DBLP]


  62. Community analysis of influential nodes for information diffusion on a social network. [Citation Graph (, )][DBLP]


  63. Improving Search Efficiency of Incremental Variable Selection by Using Second-Order Optimal Criterion. [Citation Graph (, )][DBLP]


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


  65. Growth Analysis of Neighbor Network for Evaluation of Damage Progress. [Citation Graph (, )][DBLP]


  66. Effective Visualization of Information Diffusion Process over Complex Networks. [Citation Graph (, )][DBLP]


  67. Selecting Information Diffusion Models over Social Networks for Behavioral Analysis. [Citation Graph (, )][DBLP]


  68. Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model. [Citation Graph (, )][DBLP]


  69. Efficient Estimation of Cumulative Influence for Multiple Activation Information Diffusion Model with Continuous Time Delay. [Citation Graph (, )][DBLP]


  70. What Does an Information Diffusion Model Tell about Social Network Structure?. [Citation Graph (, )][DBLP]


  71. Finding Relation between PageRank and Voter Model. [Citation Graph (, )][DBLP]


  72. Acquiring Expected Influence Curve from Single Diffusion Sequence. [Citation Graph (, )][DBLP]


  73. Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. [Citation Graph (, )][DBLP]


  74. Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network. [Citation Graph (, )][DBLP]


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


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