| Project/Area Number |
18K18001
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 60010:Theory of informatics-related
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| Research Institution | Kyushu University |
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Principal Investigator |
Suehiro Daiki 九州大学, システム情報科学研究院, 助教 (20786967)
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| Project Period (FY) |
2018-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 局所パターン / 機械学習 / 汎化性能保証 / shapelet / マルチインスタンス学習 / 汎化性能 / Shapelet / 時系列分類 / 統計的学習理論 / 局所特徴 / 時系列分析 / 画像認識 |
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| Outline of Final Research Achievements |
The main results are as follows:
(1)We proposed a general formulation of the local-feature-based learning problem by using Multiple-Instance Learning framework. (2)We showed the theoretical generalization performance of the local-feature-based hypothesis class. We applied this theory to Shapelet Learning, which is a popular task in the time-series domain, and we gave the first generalization bound of shapelet-based hypothesis class. (3)We proposed an efficient algorithm to solve the learning problem. (4)We demonstrated that our algorithm effectively works in practice.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は，時系列分類問題におけるShapelet学習のような，局所パターン学習問題と，マルチインスタンス学習問題の関連性を世界で初めて示した．従来各ドメインで独立に発展してきた様々な局所パターン学習問題に対し，統一的な定式化，汎化性能保証，解法を与えたことは，機械学習分野に大きな貢献を与えたと言える．また，本研究の骨子となった「マルチインスタンス学習の枠組みに基づく学習問題の一般化」は，局所パターン学習問題に限らない，様々な学習問題に幅広く展開可能であり，新たな研究分野の開拓に期待できる．
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