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    SIAMCOMP
    2000
    88views more  SIAMCOMP 2000»
    On a Conjecture by Coffman, Flatto, and Wright on Stochastic Machine Minimization
    13 years 10 months ago
    On a Conjecture by Coffman, Flatto, and Wright on Stochastic Machine Minimization
    Download  www.eecs.umich.edu
    We investigate a conjecture stated by Coffman, Flatto, and Wright within the context of a stochastic machine minimization problem with a hard deadline. We prove that the conjecture...
    Nah-Oak Song, Demosthenis Teneketzis
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    IJSTM
    2008
    77views more  IJSTM 2008»
    The internal differentiation of the KIBS sector: empirical evidence from cluster analysis
    13 years 10 months ago
    The internal differentiation of the KIBS sector: empirical evidence from cluster analysis
    Download  www.iaw.edu
    : The sector of Knowledge-Intensive Business Services (KIBS) is characterised by high rates of firm fluctuation, rapid changes in technological progress (e.g., in the software indu...
    Daniel Horgos, Andreas Koch
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    ISMB
    2008
    209views Computational Biology» more  ISMB 2008»
    Classification of arrayCGH data using fused SVM
    14 years 15 days ago
    Classification of arrayCGH data using fused SVM
    Download  bioinfo-out.curie.fr
    Motivation: Array-based comparative genomic hybridization (arrayCGH) has recently become a popular tool to identify DNA copy number variations along the genome. These profiles are...
    Franck Rapaport, Emmanuel Barillot, Jean-Philippe ...
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    ICMLA
    2008
    106views Machine Learning» more  ICMLA 2008»
    Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
    13 years 11 months ago
    Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
    Download  dml.cs.byu.edu
    We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
    Jun Won Lee, Christophe G. Giraud-Carrier
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    KDD
    2008
    ACM
    259views Data Mining» more  KDD 2008»
    Using ghost edges for classification in sparsely labeled networks
    14 years 10 months ago
    Using ghost edges for classification in sparsely labeled networks
    Download  reality.media.mit.edu
    We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
    Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
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