2012 Fiscal Year Final Research Report
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 |
Research Field |
Intelligent informatics
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Research Institution | Kyushu University |
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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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 |
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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Research Products
(24 results)
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[Presentation] Online Rank Aggregation2011
Author(s)
S. Yasutake, K. Hatano, E. Takimoto, and M. Takeda
Organizer
NIPS 2011 Workshop on Computational Trade-offs in Statistical Learning (COST)
Year and Date
20110000
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