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Support vector machine and its bias correction in high-dimension, low-sample-size settings
http://hdl.handle.net/2241/00150876
http://hdl.handle.net/2241/001508764e162659-a869-408b-8d2d-2968740e247e
名前 / ファイル | ライセンス | アクション |
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JSPI_191 (2.5 MB)
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Item type | Journal Article(1) | |||||
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公開日 | 2018-03-09 | |||||
タイトル | ||||||
タイトル | Support vector machine and its bias correction in high-dimension, low-sample-size settings | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
著者 |
Nakayama, Yugo
× Nakayama, Yugo× Yata, Kazuyoshi× Aoshima, Makoto |
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著者別名 |
青嶋, 誠
× 青嶋, 誠× 矢田, 和善 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that the hard-margin linear SVM holds a consistency property in which misclassification rates tend to zero as the dimension goes to infinity under certain severe conditions. We show that the SVM is very biased in HDLSS settings and its performance is affected by the bias directly. In order to overcome such difficulties, we propose a bias-corrected SVM (BC-SVM). We show that the BC-SVM gives preferable performances in HDLSS settings. We also discuss the SVMs in multiclass HDLSS settings. Finally, we check the performance of the classifiers in actual data analyses. | |||||
書誌情報 |
Journal of statistical planning and inference 巻 191, p. 88-100, 発行日 2017-12 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 03783758 | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA00253748 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1016/j.jspi.2017.05.005 | |||||
権利 | ||||||
権利情報 | © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||
著者版フラグ | ||||||
値 | author | |||||
出版者 | ||||||
出版者 | Elsevier |