Efficient Learning of Maximum Margin Sparse Hyperplanes wih Bias
| Project/Area Number |
21700171
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyushu University |
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Principal Investigator |
HATANO Kohei Kyushu University, システム情報科学研究院, 助教 (60404026)
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| Project Period (FY) |
2009 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2010: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2009: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | 機械学習 / 計算学習理論 / オンライン予測 / ブースティング / ソフトマージン最適化 / サポートベクターマシン / ランキング / パターン発見 / 1ノルム正則化 / ランキング学習 / 線形計画問題 |
|---|
| Research Abstract |
The 1-norm soft margin optimization is a popular formulation for obtaining sparse classifiers. We propose a new boosting algorithm based on linear programming. Our algorithm can take advantage of the sparsity of the solution of the underlying optimization problem. In preliminary experiments, our algorithm outperforms a state-of-the-art LP solver and LPBoost.
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Report
(3 results)
Research Products
(33 results)
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[Presentation] オンラインランク統合問題2010
Author(s)
安武翔太, 畑埜晃平, 瀧本英二, 竹田正幸
Organizer
第13回情報論的学習理論ワークショップ
Place of Presentation
東京大学
Year and Date
2010-11-05
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