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
2182004
Robustifying AdaBoost by adding the naive error rate
T Takenouchi, S Eguchi
Neural Computation 16 (4), 767-787, 2004
802004
Parameter estimation for von Mises–Fisher distributions
A Tanabe, K Fukumizu, S Oba, T Takenouchi, S Ishii
Computational Statistics 22 (1), 145-157, 2007
572007
Robust loss functions for boosting
T Kanamori, T Takenouchi, S Eguchi, N Murata
Neural computation 19 (8), 2183-2244, 2007
412007
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
232012
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
232010
Robust boosting algorithm against mislabeling in multiclass problems
T Takenouchi, S Eguchi, N Murata, T Kanamori
Neural computation 20 (6), 1596-1630, 2008
232008
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
192012
The most robust loss function for boosting
T Kanamori, T Takenouchi, S Eguchi, N Murata
International Conference on Neural Information Processing, 496-501, 2004
162004
Binary classifiers ensemble based on Bregman divergence for multi-class classification
T Takenouchi, S Ishii
Neurocomputing 273, 424-434, 2018
112018
Multiclass classification as a decoding problem
T Takenouchi, S Ishii
2007 IEEE Symposium on Foundations of Computational Intelligence, 470-475, 2007
102007
Improving Logitboost with prior knowledge
T Kanamori, T Takenouchi
Information Fusion 14 (2), 208-219, 2013
92013
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 (1), 57-88, 2012
92012
Zero-shot domain adaptation based on attribute information
M Ishii, T Takenouchi, M Sugiyama
Asian Conference on Machine Learning, 473-488, 2019
82019
Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions
T Takenouchi, S Ishii
Machine learning 85 (3), 249-272, 2011
82011
Empirical localization of homogeneous divergences on discrete sample spaces
T Takenouchi, T Kanamori
Advances in Neural Information Processing Systems 28, 820-828, 2015
72015
A multiclass classification method based on decoding of binary classifiers
T Takenouchi, S Ishii
Neural computation 21 (7), 2049-2081, 2009
72009
GroupAdaBoost: accurate prediction and selection of important genes
T Takenouchi, M Ushijima, S Eguchi
IPSJ Digital Courier 3, 145-152, 2007
72007
Statistical inference with unnormalized discrete models and localized homogeneous divergences
T Takenouchi, T Kanamori
The Journal of Machine Learning Research 18 (1), 1804-1829, 2017
62017
GroupAdaBoost for selecting important genes
T Takenouchi, M Ushijima, S Eguchi
Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05), 218-226, 2005
52005
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Articles 1–20