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Lower bounds on expected redundancy for nonparametric classes

Published: 01 September 2006 Publication History
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  • Abstract

    The article focuses on lower bound results on expected redundancy for universal coding of independent and identically distributed data on [0, 1] from parametric and nonparametric families. After reviewing existing lower bounds, we provide a new proof for minimax lower bounds on expected redundancy over nonparametric density classes. This new proof is based on the calculation of a mutual information quantity, or it utilizes the relationship between redundancy and Shannon capacity. It therefore unifies the minimax redundancy lower bound proofs in the parametric and nonparametric cases

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    • (2001)Improved Lower Bounds for Learning from Noisy ExamplesInformation and Computation10.1006/inco.2000.2919166:2(133-155)Online publication date: 1-May-2001
    • (1998)Improved lower bounds for learning from noisy examplesProceedings of the eleventh annual conference on Computational learning theory10.1145/279943.279965(104-115)Online publication date: 24-Jul-1998

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    cover image IEEE Transactions on Information Theory
    IEEE Transactions on Information Theory  Volume 42, Issue 1
    January 1996
    330 pages

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    IEEE Press

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    Published: 01 September 2006

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    • (2001)Improved Lower Bounds for Learning from Noisy ExamplesInformation and Computation10.1006/inco.2000.2919166:2(133-155)Online publication date: 1-May-2001
    • (1998)Improved lower bounds for learning from noisy examplesProceedings of the eleventh annual conference on Computational learning theory10.1145/279943.279965(104-115)Online publication date: 24-Jul-1998

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