Linear subspace

Results: 196



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41TECHNIQUES IN NON-ABELIAN ADDITIVE COMBINATORICS, EXAMPLES SHEET 1 Lent Term 2016 W. T. G.

TECHNIQUES IN NON-ABELIAN ADDITIVE COMBINATORICS, EXAMPLES SHEET 1 Lent Term 2016 W. T. G.

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Source URL: gowers.files.wordpress.com

Language: English - Date: 2016-01-28 11:59:16
42Hierarchical Exploration for Accelerating Contextual Bandits  Yisong Yue  iLab, H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA 15213, USA Sue Ann Hong

Hierarchical Exploration for Accelerating Contextual Bandits Yisong Yue iLab, H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA 15213, USA Sue Ann Hong

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Source URL: select.cs.cmu.edu

Language: English - Date: 2012-05-21 17:52:03
43The maximum weight of a linear code Simeon Ball A linear code C over Fq of length n, dimension k is a k-dimensional subspace of Fnq . The weight of a vector is the number of non-zero coordinates.

The maximum weight of a linear code Simeon Ball A linear code C over Fq of length n, dimension k is a k-dimensional subspace of Fnq . The weight of a vector is the number of non-zero coordinates.

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Source URL: www-ma4.upc.es

Language: English - Date: 2012-10-17 04:21:57
    44Optimizing the Hearing Aid Musical Experience Eric Battenberg May 18, 2007 Abstract A novel approach to hearing aid fitting for music listening is presented. The method uses

    Optimizing the Hearing Aid Musical Experience Eric Battenberg May 18, 2007 Abstract A novel approach to hearing aid fitting for music listening is presented. The method uses

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    Source URL: www.ericbattenberg.com

    Language: English - Date: 2016-04-17 01:51:14
    45Skipping Steps in Deformable Simulation with Online Model Reduction Theodore Kim1,2 University of Saskatchewan1 Doug L. James2 Cornell University2

    Skipping Steps in Deformable Simulation with Online Model Reduction Theodore Kim1,2 University of Saskatchewan1 Doug L. James2 Cornell University2

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    Source URL: mat.ucsb.edu

    Language: English - Date: 2012-03-13 17:07:09
    46Functional Subspace Clustering with Application to Time Series  A. Proof of the statement in Eq. (3) In order to show the the result in Eq. (3), we breakdown P the process in Eq. (2) into two steps: Let us denote e =

    Functional Subspace Clustering with Application to Time Series A. Proof of the statement in Eq. (3) In order to show the the result in Eq. (3), we breakdown P the process in Eq. (2) into two steps: Let us denote e =

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    Source URL: jmlr.org

    Language: English - Date: 2015-09-16 19:38:43
    47FORMALIZED  MATHEMATICS 2007, Vol. 15, No. 3, Pages

    FORMALIZED MATHEMATICS 2007, Vol. 15, No. 3, Pages

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    Source URL: fm.mizar.org

    Language: English - Date: 2009-03-24 07:43:21
    48Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data

    Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data

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    Source URL: haxbylab.dartmouth.edu

    Language: English - Date: 2016-04-01 22:52:21
      491  Zigzag Codes: MDS Array Codes with Optimal Rebuilding Itzhak Tamo∗ † , Zhiying Wang∗ , and Jehoshua Bruck∗ Engineering Department, California Institute of Technology, Pasadena, CA 91125, USA

      1 Zigzag Codes: MDS Array Codes with Optimal Rebuilding Itzhak Tamo∗ † , Zhiying Wang∗ , and Jehoshua Bruck∗ Engineering Department, California Institute of Technology, Pasadena, CA 91125, USA

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      Source URL: paradise.caltech.edu

      Language: English - Date: 2012-01-09 16:57:54
      50Odyssey 2012 The Speaker and Language Recognition WorkshopJune 2012, Singapore Speaker vectors from Subspace Gaussian Mixture Model as complementary features for Language Identification

      Odyssey 2012 The Speaker and Language Recognition WorkshopJune 2012, Singapore Speaker vectors from Subspace Gaussian Mixture Model as complementary features for Language Identification

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      Source URL: www.fit.vutbr.cz

      Language: English - Date: 2012-07-17 03:17:15