Predictive Machine Learning Methods for Graph-structured Data

2014 Fiscal Year Final Research Report

• Project/Area Number: 22680012
• Research Category: Grant-in-Aid for Young Scientists (A)
• Allocation Type: Single-year Grants
• Research Field: Intelligent informatics
• Research Institution: Kyoto University (2013); The University of Tokyo (2010-2012)
• Principal Investigator: KASHIMA Hisashi 京都大学, 情報学研究科, 教授 (80545583)
• Project Period (FY): 2010-04-01 – 2015-03-31
• Keywords: 機械学習 / 人工知能 / データマイニング / グラフ構造データ / ネットワークデータ / 関係データ / 予測

Outline of Final Research Achievements

Existing data analysis methods including machine learning are not readily designed for complex data with inside/outside graph structures such as chemical compounds, patent documents, social networks, and business networks.
In this this research project, we developed integrated, efficient, and effective methods for such complex graph-structured data from the predictive modeling perspective, which play key roles in decision making.

Free Research Field

機械学習