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Hidetomo Ichihashi: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Hidetomo Ichihashi, Katsuhiro Honda
    Robust Clustering in Fuzzy C-Means with Regularization by Cross Entropy. [Citation Graph (0, 0)][DBLP]
    FSKD, 2002, pp:471-475 [Conf]
  2. Hidetomo Ichihashi, Kiyotaka Miyagishi, Katsuhiro Honda
    Fuzzy C-Means Clustering With Regularization by K-L Information. [Citation Graph (0, 0)][DBLP]
    FUZZ-IEEE, 2001, pp:924-927 [Conf]
  3. Tomoe Entani, Hidetomo Ichihashi, Kazutomi Sugihara, Hideo Tanaka
    Analytic Hierarchy Process Based on Interval Analysis. [Citation Graph (0, 0)][DBLP]
    FUZZ-IEEE, 2001, pp:956-959 [Conf]
  4. Chi-Hyon Oh, Eriko Ikeda, Katsuhiro Honda, Hidetomo Ichihashi
    Parameter Specification for Fuzzy Clustering by Q-Learning. [Citation Graph (0, 0)][DBLP]
    IJCNN (4), 2000, pp:9-12 [Conf]
  5. Chi-Hyon Oh, Hirokazu Komatsu, Katsuhiro Honda, Hidetomo Ichihashi
    Fuzzy Clustering Algorithm Extracting Principal Components Independent of Subsidiary Variables. [Citation Graph (0, 0)][DBLP]
    IJCNN (3), 2000, pp:377-380 [Conf]
  6. Tomoe Entani, Hidetomo Ichihashi, Hideo Tanaka
    Optimistic Priority Weights with an Interval Comparison Matrix. [Citation Graph (0, 0)][DBLP]
    JSAI Workshops, 2001, pp:344-348 [Conf]
  7. Katsuhiro Honda, Hidetomo Ichihashi
    A New Approach to Fuzzification of Memberships in Cluster Analysis. [Citation Graph (0, 0)][DBLP]
    MDAI, 2005, pp:172-182 [Conf]
  8. Chi-Hyon Oh, Katsuhiro Honda, Hidetomo Ichihashi
    Quantification of Multivariate Categorical Data Considering Clusters of Items and Individuals. [Citation Graph (0, 0)][DBLP]
    MDAI, 2005, pp:164-171 [Conf]
  9. Katsuhiro Honda, Hidetomo Ichihashi, Akira Notsu, Francesco Masulli, Stefano Rovetta
    Several Formulations for Graded Possibilistic Approach to Fuzzy Clustering. [Citation Graph (0, 0)][DBLP]
    RSCTC, 2006, pp:939-948 [Conf]
  10. Hidetomo Ichihashi, Katsuhiro Honda, Akira Notsu
    Postsupervised Hard c-Means Classifier. [Citation Graph (0, 0)][DBLP]
    RSCTC, 2006, pp:918-927 [Conf]
  11. Katsuhiro Honda, Nobukazu Sugiura, Hidetomo Ichihashi, Shoichi Araki
    Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering. [Citation Graph (0, 0)][DBLP]
    Web Intelligence, 2001, pp:394-402 [Conf]
  12. Hidetomo Ichihashi, Katsuhiro Honda
    Fuzzy c-Means Classifier for Incomplete Data Sets with Outliers and Missing Values. [Citation Graph (0, 0)][DBLP]
    CIMCA/IAWTIC, 2005, pp:457-464 [Conf]
  13. Katsuhiro Honda, Hidetomo Ichihashi
    Component-wise robust linear fuzzy clustering for collaborative filtering. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 2004, v:37, n:2, pp:127-144 [Journal]
  14. Katsuhiro Honda, Hidetomo Ichihashi
    Fuzzy local independent component analysis with external criteria and its application to knowledge discovery in databases. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 2006, v:42, n:3, pp:159-173 [Journal]
  15. Hidetomo Ichihashi, Tetsuya Miyoshi, Kazunori Nagasaka, M. Tokunaga, T. Wakamatsu
    A neurofuzzy approach to variational problems by using Gaussian membership functions. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1995, v:13, n:4, pp:287-302 [Journal]
  16. Hidetomo Ichihashi, Hideo Tanaka
    Jeffrey-like rules of conditioning for the Dempster-Shafer theory of evidence. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1989, v:3, n:2, pp:143-156 [Journal]
  17. Hidetomo Ichihashi, I. Burhan Türksen
    A neuro-fuzzy approach to data analysis of pairwise comparisons. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1993, v:9, n:3, pp:227-248 [Journal]
  18. Tetsuya Miyoshi, Hidetomo Ichihashi, Kazunori Nagasaka
    Fuzzy projection pursuit density estimation by eigenvalue method. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1999, v:20, n:3, pp:237-248 [Journal]
  19. Masahiro Inuiguchi, Hidetomo Ichihashi, Yasufumi Kume
    Some properties of extended fuzzy preference relations using modalities. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1992, v:61, n:3, pp:187-209 [Journal]
  20. Masahiro Inuiguchi, Hidetomo Ichihashi, Yasufumi Kume
    Modality constrained programming problems: A unified approach to fuzzy mathematical programming problems in the setting of possibility theory. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1993, v:67, n:1-2, pp:93-126 [Journal]
  21. M. K. Luhandjula, Hidetomo Ichihashi, Masahiro Inuiguchi
    Fuzzy and semi-infinite mathematical programming. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1992, v:61, n:3, pp:233-250 [Journal]
  22. Takashi Ohtani, Hidetomo Ichihashi, Tetsuya Miyoshi
    Neurofuzzy Cognitive Maps with Adaptive B-Splines. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1998, v:110, n:1-2, pp:81-102 [Journal]
  23. Takashi Ohtani, Hidetomo Ichihashi, Tetsuya Miyoshi, Kazunori Nagasaka
    Orthogonal and Successive Projection Methods for Learning of Neurofuzzy GMDH. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1998, v:110, n:1-2, pp:5-24 [Journal]
  24. Katsuhiro Honda, Yoshihito Nakamura, Hidetomo Ichihashi
    Simultaneous Application of Fuzzy Clustering and Quantification with Incomplete Categorical Data. [Citation Graph (0, 0)][DBLP]
    JACIII, 2004, v:8, n:4, pp:397-402 [Journal]
  25. Hidetomo Ichihashi, Katsuhiro Honda
    Application of Kernel Trick to Fuzzy c-Means with Regularization by K-L Information. [Citation Graph (0, 0)][DBLP]
    JACIII, 2004, v:8, n:6, pp:566-572 [Journal]
  26. Katsuhiro Honda, Hidetomo Ichihashi
    Linear fuzzy clustering techniques with missing values and their application to local principal component analysis. [Citation Graph (0, 0)][DBLP]
    IEEE T. Fuzzy Systems, 2004, v:12, n:2, pp:183-193 [Journal]
  27. Katsuhiro Honda, Hidetomo Ichihashi
    Regularized Linear Fuzzy Clustering and Probabilistic PCA Mixture Models. [Citation Graph (0, 0)][DBLP]
    IEEE T. Fuzzy Systems, 2005, v:13, n:4, pp:508-516 [Journal]
  28. Hidetomo Ichihashi, Katsuhiro Honda, Akira Notsu, Takafumi Yagi
    Fuzzy c-Means Classifier with Deterministic Initialization and Missing Value Imputation. [Citation Graph (0, 0)][DBLP]
    FOCI, 2007, pp:214-221 [Conf]
  29. Hidetomo Ichihashi, Katsuhiro Honda, Akira Notsu, Takao Hattori
    Aggregation of Standard and Entropy Based Fuzzy c-Means Clustering by a Modified Objective Function. [Citation Graph (0, 0)][DBLP]
    FOCI, 2007, pp:447-453 [Conf]
  30. Hidetomo Ichihashi, Katsuhiro Honda, Naho Kuwamoto, Takao Hattori
    Post-supervised Fuzzy c-Means Classifier with Hard Clustering. [Citation Graph (0, 0)][DBLP]
    CIDM, 2007, pp:583-589 [Conf]
  31. Hidetomo Ichihashi, Katsuhiro Honda, Yasuhiro Kuramoto, Fumiaki Matsuura
    Fuzzy c-Means Classifier for Relational Data. [Citation Graph (0, 0)][DBLP]
    CIDM, 2007, pp:328-334 [Conf]

  32. Linear Fuzzy Clustering of Mixed Databases Based on Cluster-wise Optimal Scaling of Categorical Variables. [Citation Graph (, )][DBLP]


  33. State and action space segmentation algorithm in Q-learning. [Citation Graph (, )][DBLP]


  34. A new approach to robust k-Means clustering based on fuzzy principal component analysis. [Citation Graph (, )][DBLP]


  35. A pointing device using coordinate transformation of neurofuzzy GMDH. [Citation Graph (, )][DBLP]


  36. Structural learning of neurofuzzy GMDH with Minkowski norm. [Citation Graph (, )][DBLP]


  37. PCA-Guided k-Means with Variable Weighting and Its Application to Document Clustering. [Citation Graph (, )][DBLP]


  38. Spatiotemporal brain activities in recalling sentence by loci mnemonic system. [Citation Graph (, )][DBLP]


  39. A Perceptual Approach to User Clustering in Collaborative Filtering. [Citation Graph (, )][DBLP]


  40. Agent-based social simulation based on cognitive economic efficiency. [Citation Graph (, )][DBLP]


  41. Visualization of balancing systems based on naïve psychological approaches. [Citation Graph (, )][DBLP]


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