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Conference papers

Using a sample-dependent coding scheme for two-part MDL

Abstract : 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].
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Submitted on : Wednesday, February 16, 2011 - 4:58:50 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Long-term archiving on: : Tuesday, May 17, 2011 - 2:40:24 AM


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  • HAL Id : inria-00321522, version 1


Jakob Verbeek. Using a sample-dependent coding scheme for two-part MDL. Machine Learning & Applications (ACAI '99), The Hellenic Artificial Intelligence Society (ΕΕΤΝ), Jul 1999, Chania, Greece. ⟨inria-00321522⟩



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