| Project/Area Number |
18K11448
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Waseda University |
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Principal Investigator |
Suko Tota 早稲田大学, 社会科学総合学術院, 准教授 (40409660)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | パターン認識 / ラベルノイズ / EMアルゴリズム / 漸近解析 / 機械学習 / 半教師付き学習 / 外れ値 / ノイズ入りデータ |
|---|
| Outline of Final Research Achievements |
This study deals with classification algorithms that predict labels corresponding to certain features based on accumulated data. In practical applications of classification algorithms, low-quality data including noise is often used.
In this study, we proposed a high-performance classification algorithm that uses a unified model to represent various types of noise. Theoretical performance limits of the proposed algorithm are derived. We analyzed the performance difference between the performance limits and the proposed algorithm.
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| Academic Significance and Societal Importance of the Research Achievements |
現在，画像認識やテキスト分類などの分類アルゴリズムは広く普及しており，一般にも実用されている．しかしながら，実用の場面ではノイズを含む低品質なデータが用いられる事も多く，分類アルゴリズムの本来の性能が発揮できていない場合があり場合によっては十分な分類精度が得られない事がある．本研究の成果を発展させることで，より多くの場面で高性能な分類アルゴリズムが開発できる可能性がある．
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