SATO-ILIC Mika
- Conference, etc.
- 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/2019-08-29--2019-08-31 - Improvement of training data based on pattern of reliability scores for overlapping classification
Toko Yukako; Sato-Ilic Mika; Iijima Shinya
16th Conference of the International Federation of Classification Societies/2019-08-26--2019-08-29 - Quantification and Visualization for Difference of Fuzzy Clustering Results
Sato-Ilic Mika
The 2019 IEEE International Conference on Fuzzy Systems/2019-06-23--2019-06-26 - 自動格付のための多クラス分類器
床裕佳子; イリチユ 美佳
科研費基盤研究(A)「政府統計ミクロデータの構造化と研究利用プラットフォームの形成」に係る研究集会/2019-01-18 - Cluster-Scaled Intelligent Data Analysis
Sato-Ilic Mika
3rd International Conference on Smart Computing & Informatics/2018-12-21--2018-12-22 - Homogeneous Cluster Analysis
Sato-Ilic Mika
Complex Adaptive Systems 2018/2018-11-05--2018-11-07 - A Proposal for a Method of Forming Survey Blocks in the Economic Census for Business Frame: Development of a New Algorithm of Constrained Cluster Analysis Using the Information of Spatial Representative Points
髙橋雅夫; 浅川智雄; イリチユ 美佳
地理情報システム学会 第27回学術研究発表大会/2018-10-20--2018-10-21 - Overlapping Classification for Autocoding System
Yoko Yukako; Iijima Shinya; Sato-Ilic Mika
Use of R in Official Statistics 2018/2018-09-13--2018-09-14 - Japanese Joint Statistical Meeting 2018
小林大悟; イリチユ 美佳
分類構造に基づく異常検知手法/2018-09-09--2018-09-13 - 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
34th Fuzzy System Symposium/2018-09-03--2018-09-05 - Soft Clustering-based Models
Sato-Ilic Mika
23rd International Conference on Computattional Statistics (COMPSTAT 2018)/2018-08-28--2018-08-31 - Supervised Multiclass Classifier for Autocoding Based on Partition Coefficient
Toko Yukako; Wada Kazumi; Iijima Shinya; Sato-Ilic Mika
KES-Intelligent Decision Technologies2018/2018-06-20--2018-06-22 - Estimation of Business Demography Statistics: A Method for Analyzing Job Creation and Destruction
Takahashi Masao; Sato-Ilic Mika; Motoki
KES-Intelligent Decision Technologies2018/2018-06-20--2018-06-22 - Cluster-Scaled Regression Analysis for High-Dimension and Low-Sample Size Data
Sato-Ilic Mika
KES-Intelligent Decision Technologies2018/2018-06-20--2018-06-22 - A Fuzzy Clustering based Data Fusion Method
Sato-Ilic Mika
11th International Conference on Computational and Financial Econometrics and 10th International Conference of the ERCIM Working Group on Computational and Methodological Statistics/2017-12-16--2017-12-18 - Modeling New Complex Data Structures
Sato-Ilic Mika
Complex Adaptive Systems2017/2017-10-30 - Knowledge-based Comparable Predicted Values in Regression Analysis
Sato-Ilic Mika
Complex Adaptive Systems 2017/2017-10-30--2017-11-01 - A Simultaneous Fuzzy Clustering Method for 3-Way Multi-Source Data
矢吹健二; イリチユ 美佳
FSS2017/2017-09-13--2017-09-15 - Asymmetric Clustering Methods based on Orthogonal Projector to the Intersection of Subspaces
イリチユ 美佳
2017年度統計関連学会連合大会講演報告集/2017-09-03--2017-09-06 - Cluster-Scaled Forecasting Method For High-Dimension Low-Sample Size Data
Sato-Ilic Mika
The 6th Japanese-German Symposium on Classification/2017-08-11--2017-08-12 - Cluster Identification and Scaling Methods based on Comparative Quantification for Dissimilarity Data
Sato-Ilic Mika; Ilic Peter
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/2017-07-09--2017-07-12 - Individual Compositional Cluster Analysis
Sato-Ilic Mika
CAS2016/2016-11-02--2016-11-04 - Visualization of Fuzzy Clustering Result in Metric Space
Sato-Ilic Mika; Ilic Peter
20th International Conference on Knowledge - Based and Intelligent Information and Engineering Systems (KES)/2016-09-05--2016-09-07 - Soft Data Analysis Based on Cluster Scaling
Sato-Ilic Mika
Soft Computing Applications (SOFA) 2016/2016-08-24--2016-08-26 - 離島における分類構造を利用した人口減少に関する解析
吉元 翔汰; イリチユ 美佳
the 34th Annual Research Meeting of the Japanese Classification Society/2016-02-29--2016-03-01 - more...
- Evaluation of Fuzzy Clustering for High-Dimensional Data based on Principal Component Analysis