Development of the learning algorithm based on feature space geometry
Project/Area Number |
25330276
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Intelligent informatics
|
Research Institution | National Institute of Advanced Industrial Science and Technology |
Principal Investigator |
Akaho Shotaro 国立研究開発法人産業技術総合研究所, 人間情報研究部門, 研究グループ長 (40356340)
|
Co-Investigator(Kenkyū-buntansha) |
藤木 淳 福岡大学, 理学部, 准教授 (10357907)
|
Project Period (FY) |
2013-04-01 – 2017-03-31
|
Project Status |
Completed (Fiscal Year 2016)
|
Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
Keywords | 情報幾何 / カーネル法 / 機械学習 / アルゴリズム / 幾何学 / 関数解析学 / パタン認識 |
Outline of Final Research Achievements |
In order for machine learning to work effectively, it is very important to accurately capture the distance between input signals. In this research project, we tried to improve machine learning algorithm by introducing natural structure into space based on the framework of information geometry and reproducing kernel Hilbert space theory. As applications, the effectiveness was confirmed by applying to the problem of transfer learning which improves learning accuracy by synthesizing the results of similar learning problems when the number of data is small, and the problem of estimating the dimension of low dimensional structure embedded in high dimension space.
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Report
(5 results)
Research Products
(13 results)