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    JEC
    2006
    100views more  JEC 2006»
    RC-SIMD: Reconfigurable communication SIMD architecture for image processing applications
    13 years 8 months ago
    RC-SIMD: Reconfigurable communication SIMD architecture for image processing applications
    Download  www.es.ele.tue.nl
    During the last two decades, Single Instruction Multiple Data (SIMD) processors have become important architectures in embedded systems for image processing applications. The main ...
    Hamed Fatemi, Bart Mesman, Henk Corporaal, Twan Ba...
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    JFP
    2006
    78views more  JFP 2006»
    Sound and complete models of contracts
    13 years 8 months ago
    Sound and complete models of contracts
    Download  ttic.uchicago.edu
    Even in statically typed languages it is useful to have certain invariants checked dynamically. Findler and Felleisen gave an algorithm for dynamically checking expressive highero...
    Matthias Blume, David A. McAllester
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    EJC
    2007
    91views Information Technology» more  EJC 2007»
    Quickly deciding minor-closed parameters in general graphs
    13 years 8 months ago
    Quickly deciding minor-closed parameters in general graphs
    Download  www-math.mit.edu
    We construct algorithms for deciding essentially any minor-closed parameter, with explicit time bounds. This result strengthens previous results by Robertson and Seymour [1,2], Fr...
    Erik D. Demaine, Mohammad Taghi Hajiaghayi
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    JMLR
    2006
    85views more  JMLR 2006»
    Streamwise Feature Selection
    13 years 8 months ago
    Streamwise Feature Selection
    Download  jmlr.csail.mit.edu
    In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature sel...
    Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H...
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    JMLR
    2006
    140views more  JMLR 2006»
    Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
    13 years 8 months ago
    Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
    Download  jmlr.csail.mit.edu
    The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
    Masashi Sugiyama
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