SATO-ILIC Mika

Researcher's full information

Articles
  • Principal Component Analysis for Fuzzy Interval-Valued Data
    益居 秀; イリチユ 美佳
    Proceedings of the 37th Fuzzy System Symposium/pp.393-398, 2021-09
  • Hybrid autocoding method for the Family Income and Expenditure Survey
    床 裕佳子; イリチユ 美佳
    Proceedings of the 40th Japanese Classification Society Annual Research Meeting/pp.08, 1-08, 4, 2021-07
  • Weighted Regression Analysis for Interval-Valued Compositional Data
    藤本 聖; イリチユ 美佳
    Proceedings of Japanese Classification Society Symposium 2021/pp.19, 1-19, 4, 2021-12
  • Probabilistic Metric Based Multidimensional Scaling
    Sato-Ilic Mika
    Procedia Computer Science/168/pp.65-72, 2020-01
  • A Constrained Cluster Analysis with Homogeneity of External Criterion
    Takahashi Masao; Asakawa Tomoo; Sato-Ilic Mika
    Intelligent Decision Technologies/193/pp.303-313, 2020-06
  • Improvement of the Training Dataset for Supervised Multiclass Classification
    Toko Yukako; Sato-Ilic Mika
    Intelligent Decision Technologies/193/pp.291-302, 2020-06
  • Quantification and Visualization for Difference of Fuzzy Clustering Results
    Sato-Ilic Mika
    The 2019 IEEE International Conference on Fuzzy Systems,2019, 2019-06
  • Fuzzy Clustering and Fuzzy Clustering Models
    佐藤美佳(イリチユ 美佳)
    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics/31(3)/pp.75-81, 2019-06
  • Generalization for Improvement of the Reliability Score for Autocoding
    Toko Yukako; Iijima Shinya; Sato-Ilic Mika
    ROMANIAN STATISTICAL REVIEW/(3)/pp.47-59, 2019
  • Evaluation of Fuzzy Clustering for High-Dimensional Data based on Principal Component Analysis
    村山喬則; イリチユ 美佳
    35th Fuzzy System Symposium/35/pp.203-208, 2019-08
  • Homogeneous Cluster Analysis
    Sato-Ilic Mika
    Procedia Computer Sciences/140/pp.269-275, 2018-11
  • Cluster-Scaled Regression Analysis for High-Dimension and Low-Sample Size Data
    Sato-Ilic Mika
    Advances in Smart Systems Research/7(1)/pp.1-10, 2018-06
  • Estimation of Business Demography Statistics: A Method for Analyzing Job Creation and Destruction
    Takahashi Masao; Sato-Ilic Mika; Okamoto Motoi
    Intelligent Decision Technologies 2018/97/pp.33-43, 2018-06
  • Supervised Multiclass Classifier for Autocoding Based on Partition Coefficient
    Toko Yukako; Wada Kazumi; Iijima Shinya; Sato-Ilic Mika
    Intelligent Decision Technologies 2018/97/pp.54-64, 2018-06
  • Multilayer Clustering based on T-norms for High-Dimension Low-Sample Size Data
    伊藤佳輝; 元田卓; イリチユ 美佳
    第34回ファジィシステムシンポジウム講演論文集/pp.480-485, 2018-09
  • Knowledge-based Comparable Predicted Values in Regression Analysis
    Sato-Ilic Mika
    Procedia Computer Science, Elsevier/114/pp.216-223, 2017-11
  • Cluster Identification and Scaling Methods based on Comparative Quantification for Dissimilarity Data
    Sato-Ilic Mika; Ilic Peter
    The 2017 IEEE International Conference on Fuzzy Systems, 2017-07
  • Overlapping classification for autocoding system
    Toko Yukako; Iijima Shinya; Sato-Ilic Mika
    ROMANIAN STATISTICAL REVIEW/(4)/pp.58-73, 2018
  • A Simultaneous Fuzzy Clustering Method for 3-Way Multi-Source Data
    イリチユ 美佳; 矢吹健二
    Proceedings of the Fuzzy System Symposium/33/pp.441-446, 2017-09
  • Individual Compositional Cluster Analysis
    Sato-Ilic Mika
    Procedia Computer Science/95/pp.254-263, 2016
  • Visualization of Fuzzy Clustering Result in Metric Space
    Sato-Ilic Mika; Ilic Peter
    Procedia Computer Sciences/96/pp.1666-1675, 2016
  • Fuzzy Correlational Direction Multidimensional Scaling
    Sato-Ilic Mika
    Soft Computing Applications/2/pp.841-850, 2016
  • A Model of Cluster Loading and Its Application for a Variable Selection of High Dimension Low Sample Size Data
    J. Chen; Sato-Ilic Mika
    the 34th Annual Research Meeting of the Japanese Classification Society/pp.6-8, 2016-03
  • 判定式に基づくカーネルk-means法
    辻 陽介; イリチユ 美佳
    the 34th Annual Research Meeting of the Japanese Classification Society/pp.9-11, 2016-03
  • 外的基準を持つデータの主成分に基づく変数選択法
    山本 智基; イリチユ 美佳
    the 34th Annual Research Meeting of the Japanese Classification Society/pp.18-20, 2016-03
  • more...