| Project/Area Number |
22KJ2398
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| Project/Area Number (Other) |
21J21934 (2021-2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2021-2022) |
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| Section | 国内 |
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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 |
JI XIAOTONG 九州大学, システム情報科学府, 特別研究員(DC1)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2023: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2022: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2021: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | Reliability / Learning with Rejection / Top-rank learning / machine learning / feature / patter recognition / outlier / PCF / Learning with rejection / Writer-independent / Signature verification / rejection / CNN / signature verification |
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| Outline of Research at the Start |
This research aims to improve machine learning models' reliability from two perspectives. (1) The rejection operation removes samples that significantly impact the recognition performance. (2) Top-rank learning aims to obtain more “absolutely” positive samples.
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| Outline of Annual Research Achievements |
My research aimed at enhancing the reliability of machine learning models, particularly in contexts where errors are intolerable, such as medical image recognition and signature verification.
Year 1: the focus was on optimizing feature spaces for rejection. I developed a pioneering method employing ranking algorithms, accepted at the 15th IAPR International Workshop on Document Analysis System. This approach prioritized "absolute" positive samples, significantly improving model reliability.
Year 2: I delved deeper into rejection operations and top-rank learning. I proposed a novel framework for highly reliable signature verification, which is accepted by Pattern Recognition. This framework, integrating rejection methods and top-rank learning, addresses the fundamental challenge of ensuring high reliability in scenarios where errors are unacceptable. By selectively removing samples with ambiguous confidence scores and prioritizing "absolute" positive samples, the model's reliability was significantly enhanced.
Year 3: I focused on refining the top-rank learning approach. While this methodology provides high reliability by focusing solely on top-ranking samples, it is vulnerable to outliers. To address this, I combined rejection methods with top-rank learning, aiming to mitigate the impact of outliers. This approach ensures that the model maintains robustness and reliability by rejecting outliers during the training stage. The resulting framework was submitted to an international conference.
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