Takafumi Kanamori
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Explore the profile of Takafumi Kanamori including associated specialties, affiliations and a list of published articles.
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25
Citations
32
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Recent Articles
1.
Yang L, Qiao C, Kanamori T, Calhoun V, Stephen J, Wilson T, et al.
Neural Netw
. 2024 Dec;
183():106974.
PMID: 39657530
In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample...
2.
Nakagawa T, Sanada Y, Waida H, Zhang Y, Wada Y, Takanashi K, et al.
Neural Netw
. 2024 Nov;
169:226-241.
PMID: 39491386
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstream tasks efficiently. However,...
3.
Andeol L, Kawakami Y, Wada Y, Kanamori T, Muller K, Montavon G
Neural Netw
. 2023 Sep;
167:233-243.
PMID: 37660672
Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML model...
4.
Zhang Y, Wada Y, Waida H, Goto K, Hino Y, Kanamori T
Neural Comput
. 2023 May;
35(7):1288-1339.
PMID: 37187163
We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform well for both noncomplex...
5.
Mae Y, Kumagai W, Kanamori T
Neural Netw
. 2021 Sep;
144:394-406.
PMID: 34562813
Uncertainty evaluation is a core technique when deep neural networks (DNNs) are used in real-world problems. In practical applications, we often encounter unexpected samples that have not seen in the...
6.
Wada Y, Miyamoto S, Nakagama T, Andeol L, Kumagai W, Kanamori T
Entropy (Basel)
. 2020 Dec;
21(8).
PMID: 33267508
We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given...
7.
Kanamori T, Osugi N
Entropy (Basel)
. 2020 Dec;
21(7).
PMID: 33267416
The quality of online services highly depends on the accuracy of the recommendations they can provide to users. Researchers have proposed various similarity measures based on the assumption that similar...
8.
Matsui K, Kumagai W, Kanamori K, Nishikimi M, Kanamori T
Neural Comput
. 2019 Jul;
31(8):1718-1750.
PMID: 31260393
In this letter, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function,...
9.
Kanamori T, Takenouchi T
Neural Netw
. 2017 Sep;
95:44-56.
PMID: 28886404
This paper develops a general framework of statistical inference on discrete sample spaces, on which a neighborhood system is defined by an undirected graph. The scoring rule is a measure...
10.
Kanamori T, Fujiwara S, Takeda A
Neural Netw
. 2017 Aug;
94:173-191.
PMID: 28797759
We propose a unified formulation of robust learning methods for classification and regression problems. In the learning methods, the hinge loss is used with outlier indicators in order to detect...