| Project/Area Number |
23656072
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Engineering fundamentals
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| Research Institution | Waseda University |
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Principal Investigator |
MURATA Noboru 早稲田大学, 理工学術院, 教授 (60242038)
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| Research Collaborator |
HINO Hideitsu 早稲田大学, 理工学術院, 助教 (10580079)
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| Project Period (FY) |
2011 – 2012
|
| Project Status |
Completed (Fiscal Year 2012)
|
| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | データ縮約 / 情報量 / 多重カーネル学習 / 距離学習 |
|---|
| Research Abstract |
Generalizing the classical k-nearest neighbor method for entropy estimation, an computationally efficient method for estimating information contents of weighted data is proposed. For utilizing our distance-based method to various kinds of data sets, distance metric learning methods are considered in the framework of multiple kernel learning and just-in-time modeling. Validity of those proposed methods are confirmed by clustering problems and ensemble learning of real-world data.
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