Skip navigation
Skip navigation

Strong asymptotic assertions for discrete MDL in regression and classification

Poland, Jan; Hutter, Marcus


We study the properties of the MDL (or maximum penalized complexity) estimator for Regression and Classification, where the underlying model class is countable. We show in particular a finite bound on the Hellinger losses under the only assumption that there is a ``true'' model contained in the class. This implies almost sure convergence of the predictive distribution to the true ...[Show more]

CollectionsANU Research Publications
Date published: 2005-02
Type: Conference paper
Book Title: Proceedings of the 14th Dutch-Belgium Conference on Machine Learning Benelearn'05


File Description SizeFormat Image
Poland and Hutter Strong Asymptotic Assertions 2005.pdf175.8 kBAdobe PDFThumbnail

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  17 November 2022/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator