Faculty Profiles - FURUKAWA Tetsuo

Conference Prsentations (Oral, Poster) -

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Continuous latent variable model by non - negative kernel smoother

Japan Neural Network Society

エージェントの学習過程に基づく学習パラメータの推定及び報酬付与方法に関する考察

森逸平

第33回ファジィシステムシンポジウム日本知能情報ファジィ学会

多層自己組織化マップによる状況分類と自動アノテーションの試み

水谷龍希

第33回ファジィシステムシンポジウム日本知能情報ファジィ学会

Kansei analysis of landscape images by Tensor SOM

Technical Committee on Neurocomputing

Nonlinear Canonical Correlation Analysis of Multi-view Data by Metric Learning between SOMs

Complex tensor data analysis by tensor SOM network and information propagation in the network

Technical Committee on Neurocomputing (NC)

intrinsic viewpoint estimation of multiple survey dataset

Technical Committee on Neurocomputing (NC)

Scene Classification and Automatic Annotation Based on Multi-layered Self-Organization Map

MIZUTANI Ryuki, FURUKAWA Tetsuo, HORIO Keiichi

Proceedings of the Fuzzy System Symposium

嚥下機能解析に向けたTFSOM×SOMによる顎顔面骨格の形状分類

古賀裕章

バイオメディカル・ファジィ・システム学会大29回年次大会バイオメディカル・ファジィ・システム学会

Wing 結合型SOMによるマルチモダリティ解析

米田圭佑

第15回情報科学フォーラム電子情報通信学会, 情報処理学会

Multi-task Manifold Learning Using Higher-Order Self-Organizing Map

POSTECH-KYUTECH Joint Workshop 2016

Simultaneous visualization of topics and human relations from e-mail dataset by Tensor SOM.

Multilevel–multigroup analysis using a hierarchical tensor SOM network

Ishibashi H., Shinriki R., Isogai H., Furukawa T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event date： 2016.01.01 Language：English

© Springer International Publishing AG 2016. This paper describes a method of multilevel–multigroup analysis based on a nonlinear multiway dimensionality reduction. To analyze a set of groups in terms of the probabilistic distribution of their constituent member data, the proposed method uses a hierarchical pair of tensor self-organizing maps (TSOMs), one for the member analysis and the other for the group analysis. This architecture enables more flexible analysis than ordinary parametric multilevel analysis, as it retains a high level of translatability supported by strong visualization. Furthermore, this architecture provides a consistent and seamless computation method for multilevel–multigroup analysis by integrating two different levels into a hierarchical tensor SOM network. The proposed method is applied to a dataset of football teams in a university league, and successfully visualizes the types of players that constitute each team as well as the differences or similarities between the teams.

Tensor SOMによるe-mailデータのトピック・ロールの同時可視化

波田野創

第18回情報論的学習理論ワークショップ電子情報通信学会情報論的学習理論と機械学習(IBISML)研究会　統計数理研究所

テンソルSOMによる企業内チーム分析

古川徹生

第 25回インテリジェント・システム・シンポジウム計測自動制御学会システム・情報部門

データに潜在する観測視点の推定法-観測依存・非依存成分への分解-

石橋英朗

第25回日本神経回路学会全国大会日本神経回路学会

Impression analysis of instrument panels by Tensor SOM

FSS2015 - Fuzzy System Symposium in Tokyo

Relational data visualization by Tensor SOM

FSS2015 - Fuzzy System Symposium in Tokyo

Machine Learning Approach to Biodata Mining, and General

Robustness of Tensor SOM for Missing Data

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Conference Prsentations (Oral, Poster) -