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    SDM
    2010
    SIAM
    200views Data Mining» more  SDM 2010»
    Residual Bayesian Co-clustering for Matrix Approximation
    13 years 11 months ago
    Residual Bayesian Co-clustering for Matrix Approximation
    Download  www-users.cs.umn.edu
    In recent years, matrix approximation for missing value prediction has emerged as an important problem in a variety of domains such as recommendation systems, e-commerce and onlin...
    Hanhuai Shan, Arindam Banerjee
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    GRAPHICSINTERFACE
    2007
    122views Computer Graphics» more  GRAPHICSINTERFACE 2007»
    A mixing board interface for graphics and visualization applications
    13 years 11 months ago
    A mixing board interface for graphics and visualization applications
    Download  webhome.cs.uvic.ca
    We use a haptically enhanced mixing board with a video projector as an interface to various data visualization tasks. We report results of an expert review with four participants,...
    Matthew Crider, Steven Bergner, Thomas N. Smyth, T...
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    NIPS
    2008
    166views Information Technology» more  NIPS 2008»
    Asynchronous Distributed Learning of Topic Models
    13 years 11 months ago
    Asynchronous Distributed Learning of Topic Models
    Download  www.datalab.uci.edu
    Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two...
    Arthur Asuncion, Padhraic Smyth, Max Welling
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    DAGSTUHL
    2006
    125views Software Engineering» more  DAGSTUHL 2006»
    Decentralization and Mechanism Design for Online Machine Scheduling
    13 years 11 months ago
    Decentralization and Mechanism Design for Online Machine Scheduling
    Download  staff.science.uva.nl
    We study the online version of the classical parallel machine scheduling problem to minimize the total weighted completion time from a new perspective: We assume that the data of ...
    Birgit Heydenreich, Rudolf Müller, Marc Uetz
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    DMIN
    2006
    134views Data Mining» more  DMIN 2006»
    Hyper-Rectangular and k-Nearest-Neighbor Models in Stochastic Discrimination
    13 years 11 months ago
    Hyper-Rectangular and k-Nearest-Neighbor Models in Stochastic Discrimination
    Download  ect.bell-labs.com
    The stochastic discrimination (SD) theory considers learning as building models of uniform coverage over data distributions. Despite successful trials of the derived SD method in s...
    Iryna Skrypnyk, Tin Kam Ho
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