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Items: 17

1.

Graph-based composite local Bregman divergences on discrete sample spaces.

Kanamori T, Takenouchi T.

Neural Netw. 2017 Nov;95:44-56. doi: 10.1016/j.neunet.2017.06.005. Epub 2017 Jun 23.

PMID:
28886404
2.

Robustness of learning algorithms using hinge loss with outlier indicators.

Kanamori T, Fujiwara S, Takeda A.

Neural Netw. 2017 Oct;94:173-191. doi: 10.1016/j.neunet.2017.07.005. Epub 2017 Jul 21.

PMID:
28797759
3.

DC Algorithm for Extended Robust Support Vector Machine.

Fujiwara S, Takeda A, Kanamori T.

Neural Comput. 2017 May;29(5):1406-1438. doi: 10.1162/NECO_a_00958. Epub 2017 Mar 23.

PMID:
28333592
4.

Extended robust support vector machine based on financial risk minimization.

Takeda A, Fujiwara S, Kanamori T.

Neural Comput. 2014 Nov;26(11):2541-69. doi: 10.1162/NECO_a_00647. Epub 2014 Jul 24.

PMID:
25058701
5.

Using financial risk measures for analyzing generalization performance of machine learning models.

Takeda A, Kanamori T.

Neural Netw. 2014 Sep;57:29-38. doi: 10.1016/j.neunet.2014.05.006. Epub 2014 May 27.

PMID:
24914491
6.

Density-difference estimation.

Sugiyama M, Kanamori T, Suzuki T, du Plessis MC, Liu S, Takeuchi I.

Neural Comput. 2013 Oct;25(10):2734-75. doi: 10.1162/NECO_a_00492. Epub 2013 Jun 18.

PMID:
23777524
7.

Relative density-ratio estimation for robust distribution comparison.

Yamada M, Suzuki T, Kanamori T, Hachiya H, Sugiyama M.

Neural Comput. 2013 May;25(5):1324-70. doi: 10.1162/NECO_a_00442.

PMID:
23547952
8.

A unified classification model based on robust optimization.

Takeda A, Mitsugi H, Kanamori T.

Neural Comput. 2013 Mar;25(3):759-804. doi: 10.1162/NECO_a_00412. Epub 2012 Dec 28.

PMID:
23272917
9.

Least-squares two-sample test.

Sugiyama M, Suzuki T, Itoh Y, Kanamori T, Kimura M.

Neural Netw. 2011 Sep;24(7):735-51. doi: 10.1016/j.neunet.2011.04.003. Epub 2011 Apr 28.

PMID:
21571502
10.

Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search.

Sugiyama M, Yamada M, von B√ľnau P, Suzuki T, Kanamori T, Kawanabe M.

Neural Netw. 2011 Mar;24(2):183-98. doi: 10.1016/j.neunet.2010.10.005. Epub 2010 Oct 21. Review.

PMID:
21059481
11.

Deformation of log-likelihood loss function for multiclass boosting.

Kanamori T.

Neural Netw. 2010 Sep;23(7):843-64. doi: 10.1016/j.neunet.2010.05.009. Epub 2010 May 26.

PMID:
20542407
12.

Mutual information estimation reveals global associations between stimuli and biological processes.

Suzuki T, Sugiyama M, Kanamori T, Sese J.

BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S52. doi: 10.1186/1471-2105-10-S1-S52.

13.

Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression.

Takeuchi I, Nomura K, Kanamori T.

Neural Comput. 2009 Feb;21(2):533-59. doi: 10.1162/neco.2008.10-07-628.

PMID:
19196229
14.

Robust boosting algorithm against mislabeling in multiclass problems.

Takenouchi T, Eguchi S, Murata N, Kanamori T.

Neural Comput. 2008 Jun;20(6):1596-630. doi: 10.1162/neco.2007.11-06-400.

PMID:
18194110
15.

Robust loss functions for boosting.

Kanamori T, Takenouchi T, Eguchi S, Murata N.

Neural Comput. 2007 Aug;19(8):2183-244.

PMID:
17571942
16.

Information geometry of U-Boost and Bregman divergence.

Murata N, Takenouchi T, Kanamori T, Eguchi S.

Neural Comput. 2004 Jul;16(7):1437-81.

PMID:
15165397
17.

Robust regression with asymmetric heavy-tail noise distributions.

Takeuchi I, Bengio Y, Kanamori T.

Neural Comput. 2002 Oct;14(10):2469-96.

PMID:
12396571

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