ID | 47001 |
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Sort Key | 8
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フルテキストURL | |
著者 |
Kanatani, Kenichi
Department of Information Technology Okayama University
Kaken ID
publons
researchmap
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抄録 | Contrasting "geometric fitting", for which the noise level is taken as the asymptotic variable, with "statistical inference", for which the number of observations is taken as the asymptotic variable, we give a new definition of the "geometric AIC" and the "geometric MDL" as the counterparts of Akaike's AIC and Rissanen's MDL. We discuss various theoretical and practical problems that emerge from our analysis. Finally, we show, doing experiments using synthetic and real images, that the geometric MDL does not necessarily outperform the geometric AIC and that the two criteria have very different characteristics.
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出版物タイトル |
Memoirs of the Faculty of Engineering, Okayama University
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発行日 | 2001-12
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巻 | 36巻
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号 | 1号
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出版者 | Faculty of Engineering, Okayama University
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開始ページ | 59
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終了ページ | 77
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ISSN | 0475-0071
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NCID | AA10699856
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資料タイプ |
紀要論文
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OAI-PMH Set |
岡山大学
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言語 |
英語
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論文のバージョン | publisher
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NAID | |
Eprints Journal Name | mfe
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