Development of Multitask Pattern Recognition Model with Knowledge Transfer of Feature Space and Application to Person Identification
| Project/Area Number |
20500205
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Kobe University |
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Principal Investigator |
OZAWA Seiichi Kobe University, 大学院・工学研究科, 准教授 (70214129)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2009: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2008: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | ニューラルネット / マルチタスク学習 / 機械学習 / パターン認識 / 特徴抽出 / 知識移転 / 追加学習 / 判別分析 / ソフトコンピューティング / 特微抽出 / 主成分分析 |
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| Research Abstract |
In the environments where multiple pattern recognition tasks with some relatedness are learned sequentially, it is known that the learning is conducted efficiently even with a small number of training data by using "knowledge transfer" from one task to another. In the research project, we developed a multitask learning algorithm with an efficient knowledge transfer mechanism where a useful feature space is learned incrementally in an efficient way by transferring a part of previous learned knowledge to an unknown task. The proposed multitask learning algorithm is implemented as a person identification system using face images and the effectiveness of this system is verified.
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Report
(4 results)
Research Products
(81 results)
