Curriculum Vitaes

Tetsuji Kuboyama

  (久保山 哲二)

Profile Information

Affiliation
Professor, Computer Centre / Archival Science, Graduate School of Humanities, Gakushuin University
Tokyo Denki University
Degree
Ph.D.(University of Tokyo)

Researcher number
80302660
ORCID ID
 https://orcid.org/0000-0003-1590-0231
J-GLOBAL ID
200901047478411760
researchmap Member ID
5000102916

External link

Education

 1

Papers

 126

Misc.

 131
  • 樋口 直哉, 今村 安伸, 篠原 武, 平田 耕一, 久保山 哲二
    人工知能学会研究会資料 人工知能基本問題研究会, 123 24-29, Jan 5, 2023  
  • 徳永弘子, 久保山哲二, 木村敦, 武川直樹
    電子情報通信学会技術研究報告(Web), 121(363(HCS2021 43-60)) 43-48, 2022  
  • KAWASAKI Yuma, MIYAHARA Tetsuihiro, KUBOYAMA Tetsuji, SUZUKI Yusuke, UCHIDA Tomoyuki
    Proceedings of the Annual Conference of JSAI, JSAI2021 4G3GS2l01-4G3GS2l01, 2021  
    Knowledge acquisition from graph structured data is an important task in machine learning and data mining. TTSP (Two-Terminal Series Parallel) graphs are used as data models for electric networks and scheduling. We propose an evolutionary learning method for obtaining characteristic multiple TTSP graph patterns with wildcards, from positive and negative TTSP graph data by clustering TTSP graphs.
  • YOKOYAMA Shunsuke, MIYAHARA Tetsuhiro, SUZUKI Yusuke, UCHIDA Tomoyuki, KUBOYAMA Tetsuji
    Proceedings of the Annual Conference of JSAI, JSAI2021 4G3GS2l02-4G3GS2l02, 2021  
    Machine learning and data mining from tree structured data are studied intensively. We propose an evolutionary learning method for acquiring characteristic tag tree patterns with vertex labels and wildcards from positive and negative tree data, by using label information of positive examples. We report preliminary experimental results on our evolutionary learning method.
  • TOKUHARA Fumiya, OKINAGA Shiho, MIYAHARA Tetsuhiro, SUZUKI Yusuke, KUBOYAMA Tetsuji, UCHIDA Tomoyuki
    Proceedings of the Annual Conference of JSAI, JSAI2020 1O3GS802-1O3GS802, 2020  
    Machine learning from graph structured data are studied intensively. Many chemical compounds can be expressed by outerplanar graphs. The purpose of this paper is to propose a learning method for obtaining characteristic graph patterns from positive and negative outerplanar graph data. We propose a two-stage evolutionary learning method for acquiring characteristic multiple block preserving outerplanar graph patterns with wildcards from positive and negative outerplanar graph data, by using label information of positive examples. We report preliminary experimental results on our evolutionary learning method.

Teaching Experience

 15

Research Projects

 33