Using a sample-dependent coding scheme for two-part MDL
Résumé
In this paper we report on how using sample-dependent coding schemes can lead to poor results in applying Rissanen's Minimum Description Length (MDL) principle [Ris89]. The MDL principle is one of the many known model selection methods in the field of `machine learning', `statistics', or `inductive inference'. We analyze the experimental results presented in [KMNR97] and provide a method to avoid the overfitting. We do so by using a different coding scheme than in [KMNR97].
Domaines
Apprentissage [cs.LG]
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