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Geometric Classifier for Multiclass, High-Dimensional Data
http://hdl.handle.net/2241/00128944
http://hdl.handle.net/2241/00128944a7bcfa0c-1429-4d43-965b-8fced00cddc7
名前 / ファイル | ライセンス | アクション |
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SA_34-3 (99.2 kB)
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Item type | Journal Article(1) | |||||
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公開日 | 2015-10-21 | |||||
タイトル | ||||||
タイトル | Geometric Classifier for Multiclass, High-Dimensional Data | |||||
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 |
Aoshima, Makoto
× Aoshima, Makoto× Yata, Kazuyoshi |
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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 article, we consider a geometric classifier that is applicable to multiclass classification for high-dimensional data. We show the consistency property and the asymptotic normality of the geometric classifier under certain mild conditions. We discuss sample size determination so that the geometric classifier can ensure that its misclassification rates are less than prespecified thresholds. We give a two-stage procedure to estimate the sample sizes required in such a geometric classifier and propose a misclassification rate–adjusted classifier (MRAC) based on the geometric classifier. We evaluate the performance of the MRAC theoretically and numerically. Finally, we demonstrate the MRAC in actual data analyses by using a microarray data set. | |||||
書誌情報 |
Sequential analysis 巻 34, 号 3, p. 279-294 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0747-4946 | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA10538981 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1080/07474946.2015.1063256 | |||||
権利 | ||||||
権利情報 | © Taylor & Francis Group, LLC | |||||
権利 | ||||||
権利情報 | This is an Accepted Manuscript of an article published by Taylor & Francis in Sequential Analysis on Aug 2015 available online: http://www.tandfonline.com/10.1080/07474946.2015.1063256 | |||||
著者版フラグ | ||||||
値 | author | |||||
出版者 | ||||||
出版者 | Taylor & Francis |