このエントリーをはてなブックマークに追加
ID 47001
フルテキストURL
著者
Kanatani, Kenichi Department of Information Technology Okayama University
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.
発行日
2001-12
出版物タイトル
Memoirs of the Faculty of Engineering, Okayama University
36巻
1号
出版者
Faculty of Engineering, Okayama University
開始ページ
59
終了ページ
77
ISSN
0475-0071
NCID
AA10699856
資料タイプ
紀要論文
言語
English
論文のバージョン
publisher
査読
無し
Eprints Journal Name
mfe