Approximations

Results: 414



#Item
101Linear-Size Approximations to the Vietoris-Rips Filtration Don Sheehy Geometrica Group INRIA Saclay

Linear-Size Approximations to the Vietoris-Rips Filtration Don Sheehy Geometrica Group INRIA Saclay

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Source URL: icms.org.uk

- Date: 2012-07-06 03:01:30
    102Guiding Particle Filtering with Marginal Approximations: an Application in Protein Image Interpretation Frank DiMaio1 , Ameet Soni1 , Jude Shavlik1 , George Phillips2,1 1 UW-Madison Computer Sciences Department, 2 UW-Mad

    Guiding Particle Filtering with Marginal Approximations: an Application in Protein Image Interpretation Frank DiMaio1 , Ameet Soni1 , Jude Shavlik1 , George Phillips2,1 1 UW-Madison Computer Sciences Department, 2 UW-Mad

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    Source URL: snowbird.djvuzone.org

    Language: English - Date: 2007-03-09 02:31:33
      103Program Announcement Electromagnetic Metamaterials and their Approximations: Practical and Theoretical Aspects September 22-25, 2008 Organizers: Radu Balan, Dionisios Margetis, Eitan Tadmor, Gunther Uhlmann, Michael Voge

      Program Announcement Electromagnetic Metamaterials and their Approximations: Practical and Theoretical Aspects September 22-25, 2008 Organizers: Radu Balan, Dionisios Margetis, Eitan Tadmor, Gunther Uhlmann, Michael Voge

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      Source URL: www.cscamm.umd.edu

      Language: English - Date: 2008-06-19 11:55:36
        104GUARANTEED QUALITY APPROXIMATIONS FOR MEDIAL AXIS OF IMPLICIT PLANAR CURVES MICHAEL BURR∗ AND DAVID LETSCHER† Abstract. We apply subdivision methods to estimate the medial axis for a region bounded by an implicit cur

        GUARANTEED QUALITY APPROXIMATIONS FOR MEDIAL AXIS OF IMPLICIT PLANAR CURVES MICHAEL BURR∗ AND DAVID LETSCHER† Abstract. We apply subdivision methods to estimate the medial axis for a region bounded by an implicit cur

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        Source URL: fwcg14.cse.uconn.edu

        Language: English - Date: 2014-10-29 21:34:50
          105Submitted to Statistical Science Models as Approximations — A Conspiracy of Random Regressors and Model Deviations Against Classical

          Submitted to Statistical Science Models as Approximations — A Conspiracy of Random Regressors and Model Deviations Against Classical

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          Source URL: www-stat.wharton.upenn.edu

          Language: English - Date: 2015-09-24 15:33:15
            106IMA Journal of Numerical Analysis, Superconvergent Recovery of the Gradient from Piecewise Linear Finite-element Approximations N I C K LEVINE

            IMA Journal of Numerical Analysis, Superconvergent Recovery of the Gradient from Piecewise Linear Finite-element Approximations N I C K LEVINE

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            Source URL: www.nicklevine.org

            Language: English - Date: 2014-06-14 06:16:53
              1072. Effective Approximations of Matching Priors 1. Summary How can we achieve truly robust dense, real-time matching? The strong priors generally available on image correspondence problems, are often used partially and p

              2. Effective Approximations of Matching Priors 1. Summary How can we achieve truly robust dense, real-time matching? The strong priors generally available on image correspondence problems, are often used partially and p

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              Source URL: www.margaritachli.com

              Language: English - Date: 2011-06-29 13:14:06
              108Spanning Tree Approximations for Conditional Random Fields Patrick Pletscher Department of Computer Science ETH Zurich, Switzerland

              Spanning Tree Approximations for Conditional Random Fields Patrick Pletscher Department of Computer Science ETH Zurich, Switzerland

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              Source URL: www.pletscher.org

              Language: English - Date: 2014-01-07 01:47:59
                109On Multiplicative λ-Approximations and Some Geometric Applications∗ Ilan Newman† Abstract Let F be a set system over an underlying finite set X, and let µ be a nonnegative P measure over X. I.e.,∗ for every

                On Multiplicative λ-Approximations and Some Geometric Applications∗ Ilan Newman† Abstract Let F be a set system over an underlying finite set X, and let µ be a nonnegative P measure over X. I.e.,∗ for every

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                Source URL: cs.haifa.ac.il

                Language: English - Date: 2011-10-04 15:03:44
                  110Partitioned Linear Programming Approximations for MDPs

                  Partitioned Linear Programming Approximations for MDPs

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                  Source URL: uai2008.cs.helsinki.fi

                  Language: English - Date: 2008-05-16 11:52:57