2016 Fiscal Year Final Research Report
Stream data classification with real time learning of kernel matrix
Project/Area Number |
26330251
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Intelligent informatics
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Research Institution | Prefectural University of Hiroshima (2016) Toyohashi University of Technology (2014-2015) |
Principal Investigator |
Okabe Masayuki 県立広島大学, 経営情報学部, 講師 (50362330)
|
Project Period (FY) |
2014-04-01 – 2017-03-31
|
Keywords | カーネル行列学習 / アンサンブル学習 / 制約付きK-means |
Outline of Final Research Achievements |
Kernel matrix learning is an indispensable technique for machine learning to make of high accuracy. In this research, we developed an algorithm of kernel matrix learning that can be applied to incremental stream data classification and then proposed an active learning method that selects candidate data pairs to be labeled as constraints. We verified the utility of our developed algorithms through the experiments of outlier detection from network traffic.
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Free Research Field |
知能情報学
|