Online Data Classification by Semi-Supervised Metric Learning
Project/Area Number |
23700167
|
Research Category |
Grant-in-Aid for Young Scientists (B)
|
Allocation Type | Multi-year Fund |
Research Field |
Intelligent informatics
|
Research Institution | Toyohashi University of Technology |
Principal Investigator |
OKABE Masayuki 豊橋技術科学大学, 情報メディア基盤センター, 助教 (50362330)
|
Project Period (FY) |
2011 – 2013
|
Project Status |
Completed (Fiscal Year 2013)
|
Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2013: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2012: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2011: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
Keywords | 知能情報処理 / 機械学習 / データマイニング / 距離学習 / 制約付きクラスタリング / 外れ値検出 / クラスタアンサンブル / ブースティング |
Research Abstract |
Metric learning is a kind of method useful for automatic data classification such as categorization of news on the Web or item recommendation in online shopping. In this research, we deal with two problems of costs for acquiring training data and computation when using metric learning, and propose some algorithms to resolve the problems. We also construct an anomaly detection system from network traffic data based on the proposed algorithms.
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Report
(4 results)
Research Products
(21 results)