Predictive Machine Learning Methods for Graph-structured Data
Project/Area Number |
22680012
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Research Category |
Grant-in-Aid for Young Scientists (A)
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Allocation Type | Single-year Grants |
Research Field |
Intelligent informatics
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Research Institution | Kyoto University (2013) The University of Tokyo (2010-2012) |
Principal Investigator |
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Project Period (FY) |
2010-04-01 – 2015-03-31
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Project Status |
Completed (Fiscal Year 2014)
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Budget Amount *help |
¥14,820,000 (Direct Cost: ¥11,400,000、Indirect Cost: ¥3,420,000)
Fiscal Year 2013: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2012: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2011: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2010: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
|
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 / グラフ / ネットワーク / 木 |
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.
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Report
(5 results)
Research Products
(29 results)
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[Presentation] プライバシ保護クラウドソーシング2014
Author(s)
梶野 洸, 馬場 雪乃, 鹿島 久嗣
Organizer
人工知能学会全国大会 (第28回)
Place of Presentation
ひめぎんホール, 愛媛
Year and Date
2014-05-12 – 2014-05-15
Related Report
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