Subspace

Results: 408



#Item
11Subspace Trail Cryptanalysis and its Applications to AES Lorenzo Grassi, Christian Rechberger and Sondre Rønjom March, 2017

Subspace Trail Cryptanalysis and its Applications to AES Lorenzo Grassi, Christian Rechberger and Sondre Rønjom March, 2017

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Source URL: www.nuee.nagoya-u.ac.jp

Language: English - Date: 2017-03-06 00:03:12
    12A Framework for 3D Object Recognition using the Kernel Constrained Mutual Subspace Method Kazuhiro Fukui*1, Bjorn Stenger*2 and Osamu Yamaguchi*2 University of Tsukuba Corporate Research & Development Center

    A Framework for 3D Object Recognition using the Kernel Constrained Mutual Subspace Method Kazuhiro Fukui*1, Bjorn Stenger*2 and Osamu Yamaguchi*2 University of Tsukuba Corporate Research & Development Center

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    Source URL: www.cvlab.cs.tsukuba.ac.jp

    Language: English - Date: 2018-05-13 00:54:47
      13Preprints of the 15th IFAC Symposium on System Identification Saint-Malo, France, July 6-8, 2009 Subspace Identification From Classical Realization Methods

      Preprints of the 15th IFAC Symposium on System Identification Saint-Malo, France, July 6-8, 2009 Subspace Identification From Classical Realization Methods

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      Source URL: maeresearch.ucsd.edu

      Language: English - Date: 2009-11-25 05:42:04
        14Evolutionary Random Neural Ensembles Based on Negative Correlation Learning Huanhuan Chen and Xin Yao Abstract— This paper proposes to incorporate bootstrap of data, random feature subspace and evolutionary algorithm w

        Evolutionary Random Neural Ensembles Based on Negative Correlation Learning Huanhuan Chen and Xin Yao Abstract— This paper proposes to incorporate bootstrap of data, random feature subspace and evolutionary algorithm w

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        Source URL: staff.ustc.edu.cn

        Language: English - Date: 2014-04-11 09:32:07
          15NOETHERIAN MODULES KEITH CONRAD 1. Introduction In a finite-dimensional vector space, every subspace is finite-dimensional and the dimension of a subspace is at most the dimension of the whole space. Unfortunately, the n

          NOETHERIAN MODULES KEITH CONRAD 1. Introduction In a finite-dimensional vector space, every subspace is finite-dimensional and the dimension of a subspace is at most the dimension of the whole space. Unfortunately, the n

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          Source URL: www.math.uconn.edu

          Language: English - Date: 2014-03-03 14:09:51
            163-24 MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN Hand Shape Recognition based on Kernel Orthogonal Mutual Subspace Method

            3-24 MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN Hand Shape Recognition based on Kernel Orthogonal Mutual Subspace Method

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            Source URL: www.cvlab.cs.tsukuba.ac.jp

            Language: English - Date: 2018-05-13 00:54:46
              17The Kernel Orthogonal Mutual Subspace Method and its Application to 3D Object Recognition Kazuhiro Fukui1 and Osamu Yamaguchi2 1

              The Kernel Orthogonal Mutual Subspace Method and its Application to 3D Object Recognition Kazuhiro Fukui1 and Osamu Yamaguchi2 1

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              Source URL: www.cvlab.cs.tsukuba.ac.jp

              Language: English - Date: 2018-05-13 00:54:46
                18A Closed Form Solution to Robust Subspace Estimation and Clustering Paolo Favaro Heriot-Watt University Ren´e Vidal Johns Hopkins University

                A Closed Form Solution to Robust Subspace Estimation and Clustering Paolo Favaro Heriot-Watt University Ren´e Vidal Johns Hopkins University

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                Source URL: www.vision.jhu.edu

                Language: English - Date: 2012-04-30 19:29:47
                  19A Framework for Ontology-Driven Subspace Clustering Jinze Liu, Wei Wang Jiong Yang Department of Computer Science,

                  A Framework for Ontology-Driven Subspace Clustering Jinze Liu, Wei Wang Jiong Yang Department of Computer Science,

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                  Source URL: web.cs.ucla.edu

                  Language: English - Date: 2004-08-31 19:40:52
                    20Sayan Mukherjee* (). Geometry in statistical inference. Geometric approaches to data analysis including manifold learning, subspace inference, factor models, and inferring covariance/positive defi

                    Sayan Mukherjee* (). Geometry in statistical inference. Geometric approaches to data analysis including manifold learning, subspace inference, factor models, and inferring covariance/positive defi

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                    Source URL: jointmathematicsmeetings.org

                    - Date: 2013-10-31 00:32:33