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    UAI
    2001
    144views Artificial Intelligence» more  UAI 2001»
    Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk
    14 years 11 months ago
    Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk
    Download  www.aladdin.cs.cmu.edu
    We present an iterative Markov chain Monte Carlo algorithm for computing reference priors and minimax risk for general parametric families. Our approach uses MCMC techniques based...
    John D. Lafferty, Larry A. Wasserman
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    UAI
    2004
    135views Artificial Intelligence» more  UAI 2004»
    Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
    14 years 11 months ago
    Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
    Download  uai.sis.pitt.edu
    We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of s...
    Cosma Rohilla Shalizi, Kristina Lisa Shalizi
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    SCANGIS
    2003
    160views Information Technology» more  SCANGIS 2003»
    On the Adequacy of GIS-Generated Weed Maps for Precision Farming
    14 years 11 months ago
    On the Adequacy of GIS-Generated Weed Maps for Precision Farming
    Download  www.scangis.org
    Discrete sampling data is used in several environmental studies to create maps in order to support decision-making processes. The decision maps represent an increasing importance i...
    Matthias Backes, Lutz Plümer
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    NIPS
    1996
    112views Information Technology» more  NIPS 1996»
    Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
    14 years 11 months ago
    Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
    Download  www.ri.cmu.edu
    Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
    Jeff G. Schneider
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    NIPS
    1996
    84views Information Technology» more  NIPS 1996»
    Solving the Ill-Conditioning in Neural Network Learning
    14 years 11 months ago
    Solving the Ill-Conditioning in Neural Network Learning
    Download  www.robotic.dlr.de
    Abstract. In this paper we investigate the feed-forward learning problem. The well-known ill-conditioning which is present in most feed-forward learning problems is shown to be the...
    P. Patrick van der Smagt, Gerd Hirzinger
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