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    SDM
    2012
    SIAM
    216views Data Mining» more  SDM 2012»
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    13 years 6 months ago
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    Download  www.hpl.hp.com
    :  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
    George Forman
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    SDM
    2012
    SIAM
    247views Data Mining» more  SDM 2012»
    Simplex Distributions for Embedding Data Matrices over Time
    13 years 6 months ago
    Simplex Distributions for Embedding Data Matrices over Time
    Download  www-kd.iai.uni-bonn.de
    Early stress recognition is of great relevance in precision plant protection. Pre-symptomatic water stress detection is of particular interest, ultimately helping to meet the chal...
    Kristian Kersting, Mirwaes Wahabzada, Christoph R&...
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    SDM
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    A Flexible Open-Source Toolbox for Scalable Complex Graph Analysis
    13 years 6 months ago
    A Flexible Open-Source Toolbox for Scalable Complex Graph Analysis
    Download  www.cs.ucsb.edu
    The Knowledge Discovery Toolbox (KDT) enables domain experts to perform complex analyses of huge datasets on supercomputers using a high-level language without grappling with the ...
    Adam Lugowski, David M. Alber, Aydin Buluç,...
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    SDM
    2012
    SIAM
    322views Data Mining» more  SDM 2012»
    Adaptive Multi-task Sparse Learning with an Application to fMRI Study
    13 years 6 months ago
    Adaptive Multi-task Sparse Learning with an Application to fMRI Study
    Download  www.cs.cmu.edu
    In this paper, we consider the multi-task sparse learning problem under the assumption that the dimensionality diverges with the sample size. The traditional l1/l2 multi-task lass...
    Xi Chen, Jingrui He, Rick Lawrence, Jaime G. Carbo...
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    SDM
    2012
    SIAM
    278views Data Mining» more  SDM 2012»
    Legislative Prediction via Random Walks over a Heterogeneous Graph
    13 years 6 months ago
    Legislative Prediction via Random Walks over a Heterogeneous Graph
    Download  www.ee.columbia.edu
    In this article, we propose a random walk-based model to predict legislators’ votes on a set of bills. In particular, we first convert roll call data, i.e. the recorded votes a...
    Jun Wang, Kush R. Varshney, Aleksandra Mojsilovic
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