中山 優吾

Last Update: 2020/04/15 19:54:12

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Name(Kanji/Kana/Abecedarium Latinum)
中山 優吾/ナカヤマ ユウゴ/Nakayama, Yugo
Primary Affiliation(Org1/Job title)
Graduate Schools Informatics/Assistant Professor
E-mail Address
E-mail address
n-yougo @ i.kyoto-u.ac.jp
Academic Degree
Field(Japanese) Field(English) University(Japanese) University(English) Method
修士(理学) 筑波大学
博士(理学) 筑波大学
ORCID ID
https://orcid.org/0000-0001-7321-2141
researchmap URL
https://researchmap.jp/yougon
Published Papers
Author Author(Japanese) Author(English) Title Title(Japanese) Title(English) Bibliography Bibliography(Japanese) Bibliography(English) Publication date Refereed paper Language Publishing type Disclose
Yugo Nakayama Yugo Nakayama Yugo Nakayama Robust support vector machine for high-dimensional imbalanced data Robust support vector machine for high-dimensional imbalanced data Robust support vector machine for high-dimensional imbalanced data COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2019/04 Refereed English Research paper(scientific journal) Disclose to all
Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima Nakayama, Y., Yata, K. & Aoshima, M. Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings. Nakayama, Y., Yata, K. & Aoshima, M. Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings. Nakayama, Y., Yata, K. & Aoshima, M. Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings. Annals of the Institute of Statistical Mathematics Annals of the Institute of Statistical Mathematics Annals of the Institute of Statistical Mathematics 2019 Refereed Disclose to all
Kazuyoshi Yata, Makoto Aoshima, Yugo Nakayama Kazuyoshi Yata, Makoto Aoshima, Yugo Nakayama Kazuyoshi Yata, Makoto Aoshima, Yugo Nakayama A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS, 37, 3, 397-411 SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS, 37, 3, 397-411 SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS, 37, 3, 397-411 2018 Refereed English Research paper(scientific journal) Disclose to all
Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima Support vector machine and its bias correction in high-dimension, low-sample-size settings Support vector machine and its bias correction in high-dimension, low-sample-size settings Support vector machine and its bias correction in high-dimension, low-sample-size settings JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 191, 88-100 JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 191, 88-100 JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 191, 88-100 2017/12 Refereed English Research paper(scientific journal) Disclose to all
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