New approaches for the analysis ofcomplex time-series using kernel methods.
Project/Area Number |
23700172
|
Research Category |
Grant-in-Aid for Young Scientists (B)
|
Allocation Type | Multi-year Fund |
Research Field |
Intelligent informatics
|
Research Institution | Kyoto University |
Principal Investigator |
CUTURI Marco 京都大学, 情報学研究科, 准教授 (80597344)
|
Project Period (FY) |
2011 – 2012
|
Project Status |
Completed (Fiscal Year 2012)
|
Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2012: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2011: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
|
Keywords | Machine Learning / Kernel Methods / 時列カーネル / 国際研究者交流 |
Research Abstract |
Time series are now increasingly complex. Each observation may describe a structured object (an image or a graph for instance) or alternatively a very high dimensional feature vector. The goal of our project is to develop new methods to handle time-series of complex data through kernel methods and optimization methods.
|
Report
(3 results)
Research Products
(17 results)