Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Kohei Hatano Last modified date:2018.06.15

Associate Professor / Division for Theoretical Natural Science / Faculty of Arts and Science


Presentations
1. Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Shuji Yamamoto, Kenichi Bannai and Akiko Takeda, Learning theory and algorithms for shapelets and other local features, NIPS 2017 Time Series Workshop, 2017.12.
2. , [URL].
3. Kengo Kuwahara, 末廣 大貴, 畑埜 晃平, 瀧本 英二, D. Suehiro, K. Kuwahara, K. Hatano, E. Takimoto, “Time Series Classification Based on Rom Shapelets,” , 2016., NIPS 2016 Time Series Workshop, 2016.12.
4. Takahiro Fujita, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Online Linear Optimization over Permutations with Precedence Constraints

, NIPS 2014 Workshop on Discrete Optimization in Machine Learning(DISCML), 2014.12.
5. Issei Matsumoto, Kohei Hatano, Eiji Takimoto, Online Prediction with Bradley-Terry Models, NIPS 2014 Workshop on Analysis of Rank Data: Confluence of Social Choice, Operations Research, and Machine Learning, 2014.12.
6. Combinatorial MTS problem.
7. Online scheduling of precedence-constrained jobs on a single machine.
8. 畑埜 晃平, Combinatorial Online Prediction via Metarounding, TCE Guest Lecture, 2013.12.
9. Takahiro Fujita, Kohei Hatano, Eiji Takimoto, Combinatorial Online Prediction Using Offline Approximation Algorithms, The sixth Annual Meeting of Asian Association for Algorithms and Computation (AAAC2013), 2013.04.
10. Daiki Suehiro, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Kiyohito Nagano, Online Prediction over Base Polyhedra

, NIPS 2012 Workshop on Discrete Optimization in Machine Learning(DISCML), 2012.12.
11. Online Rank Aggregation.
12. Online Rank Aggregation.
13. Learning evaluation functions for Shogi via bipertite ranking learning with SVMs .
14. Learning evaluation functions for Shogi via bipertite ranking learning with SVMs .
15. Online Rank Aggregation.
16. Online Rank Aggregation.
17. Online Learning Based On Maximum Entropy Principle.
18. Online Learning of Maximum p-Norm Margin Classifiers with Bias.
19. Efficient online learning of linear threshold functions with large biases..
20. Boosting using classifiers with nearly one-sided error.
21. An Efficient Boosting by Filtering.
22. Boosting by filtering using an information-theoretic criterion.