mfe_36_1_059_077.pdf 1.38 MB
Kanatani, Kenichi Department of Information Technology Okayama University
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.
Memoirs of the Faculty of Engineering, Okayama University
Faculty of Engineering, Okayama University
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