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    94
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    VMV
    2004
    185views Visualization» more  VMV 2004»
    Convexification of Unstructured Grids
    14 years 11 months ago
    Convexification of Unstructured Grids
    Download  www.gris.uni-tuebingen.de
    Unstructured tetrahedral grids are a common data representation of three-dimensional scalar fields. For convex unstructured meshes efficient rendering methods are known. For conca...
    Stefan Röttger, Stefan Guthe, Andreas Schiebe...
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    WSC
    2001
    113views Modeling And Simulation» more  WSC 2001»
    Dimensionality analysis of a simulation outcome space
    14 years 11 months ago
    Dimensionality analysis of a simulation outcome space
    Download  www.informs-sim.org
    This paper investigates the dimensionality characteristics of the outcome space of a combat simulation. The independent state variables of all of the outcome states for a simulati...
    John B. Gilmer Jr., Frederick J. Sullivan
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    86
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    PICS
    2003
    79views Image Processing» more  PICS 2003»
    Selection of Training Sets for the Characterisation of Multispectral Imaging Systems
    14 years 11 months ago
    Selection of Training Sets for the Characterisation of Multispectral Imaging Systems
    Download  www.ivl.disco.unimib.it
    To establish a correlation between the system output and the corresponding reflectance, the system characterisation functionDeriving the actual multispectral data from the output o...
    Paolo Pellegri, Gianluca Novati, Raimondo Schettin...
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    NIPS
    1998
    156views Information Technology» more  NIPS 1998»
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    14 years 11 months ago
    Learning Nonlinear Dynamical Systems Using an EM Algorithm
    Download  www.cs.nyu.edu
    The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
    Zoubin Ghahramani, Sam T. Roweis
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    NIPS
    1998
    164views Information Technology» more  NIPS 1998»
    Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
    14 years 11 months ago
    Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
    Download  www.cis.upenn.edu
    In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
    Michael J. Kearns, Satinder P. Singh
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