Boosting and Online Learning Techniques for Ranking Problems and Their Applications
| Project/Area Number |
23700178
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyushu University |
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Principal Investigator |
HATANO Kohei 九州大学, システム情報科学研究院, 助教 (60404026)
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| Project Period (FY) |
2011 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2011: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
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| Keywords | ランキング / オンライン予測 / ブースティング / 劣モジュラ関数 / 離散構造機械学習 / 計算学習理論 / 機械学習 / 順列 / スケジューリング / 離散最適化 / ランキング学習 / ソフトマージン最適化 / 推薦 |
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| Research Abstract |
Ranking is the problem to predict orderings or permutations over data, and it appears in many application tasks such as information retrieval, recommendation, risk analysis, bioinformatics, natural language processing, and so on. In this project, we developed efficient ranking prediction methods based on the online learning theory.Our methods predict almost as well as the best ranking in hindsight. Further, we generalize our methods to online prediction methods for some classes of combinatorial concepts.
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Report
(3 results)
Research Products
(47 results)
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[Presentation] Online Rank Aggregation
Author(s)
Shota Yasutake, Kohei Hatano, Eiji Takimoto, and Masayuki Takeda
Organizer
NIPS 2011 Workshop on Computational Trade-offs in Statistical Learning (COST)
Place of Presentation
シエラネバダ，スペイン
Related Report
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