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Takashi Takenouchi
Takashi Takenouchi
FUTURE UNIVERSITY HAKODATE
Verified email at fun.ac.jp
Title
Cited by
Cited by
Year
Information geometry of U-Boost and Bregman divergence
N Murata, T Takenouchi, T Kanamori, S Eguchi
Neural Computation 16 (7), 1437-1481, 2004
2362004
Robustifying AdaBoost by adding the naive error rate
T Takenouchi, S Eguchi
Neural Computation 16 (4), 767-787, 2004
862004
Parameter estimation for von Mises–Fisher distributions
A Tanabe, K Fukumizu, S Oba, T Takenouchi, S Ishii
Computational Statistics 22, 145-157, 2007
642007
Robust loss functions for boosting
T Kanamori, T Takenouchi, S Eguchi, N Murata
Neural computation 19 (8), 2183-2244, 2007
492007
Self-measuring similarity for multi-task gaussian process
K Hayashi, T Takenouchi, R Tomioka, H Kashima
Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 145-153, 2012
322012
Improving imbalanced classification using near-miss instances
A Tanimoto, S Yamada, T Takenouchi, M Sugiyama, H Kashima
Expert Systems with Applications 201, 117130, 2022
282022
Exponential family tensor factorization for missing-values prediction and anomaly detection
K Hayashi, T Takenouchi, T Shibata, Y Kamiya, D Kato, K Kunieda, ...
2010 IEEE International Conference on Data Mining, 216-225, 2010
282010
An extension of the receiver operating characteristic curve and AUC-optimal classification
T Takenouchi, O Komori, S Eguchi
Neural computation 24 (10), 2789-2824, 2012
272012
Robust boosting algorithm against mislabeling in multiclass problems
T Takenouchi, S Eguchi, N Murata, T Kanamori
Neural computation 20 (6), 1596-1630, 2008
272008
The most robust loss function for boosting
T Kanamori, T Takenouchi, S Eguchi, N Murata
Neural Information Processing: 11th International Conference, ICONIP 2004 …, 2004
182004
A unified statistically efficient estimation framework for unnormalized models
M Uehara, T Kanamori, T Takenouchi, T Matsuda
International Conference on Artificial Intelligence and Statistics, 809-819, 2020
152020
Binary classifiers ensemble based on Bregman divergence for multi-class classification
T Takenouchi, S Ishii
Neurocomputing 273, 424-434, 2018
152018
Zero-shot domain adaptation based on attribute information
M Ishii, T Takenouchi, M Sugiyama
Asian Conference on Machine Learning, 473-488, 2019
122019
Regret minimization for causal inference on large treatment space
A Tanimoto, T Sakai, T Takenouchi, H Kashima
International Conference on Artificial Intelligence and Statistics, 946-954, 2021
112021
Multiclass classification as a decoding problem
T Takenouchi, S Ishii
2007 IEEE Symposium on Foundations of Computational Intelligence, 470-475, 2007
112007
Partially zero-shot domain adaptation from incomplete target data with missing classes
M Ishii, T Takenouchi, M Sugiyama
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020
102020
Statistical inference with unnormalized discrete models and localized homogeneous divergences
T Takenouchi, T Kanamori
Journal of Machine Learning Research 18 (56), 1-26, 2017
102017
Exponential family tensor factorization: an online extension and applications
K Hayashi, T Takenouchi, T Shibata, Y Kamiya, D Kato, K Kunieda, ...
Knowledge and information systems 33, 57-88, 2012
102012
Empirical localization of homogeneous divergences on discrete sample spaces
T Takenouchi, T Kanamori
Advances in Neural Information Processing Systems 28, 2015
92015
Improving Logitboost with prior knowledge
T Kanamori, T Takenouchi
Information Fusion 14 (2), 208-219, 2013
92013
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