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    » On Phase Transitions in Learning Sparse Networks
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    ECML
    2007
    Springer
    135views Machine Learning» more  ECML 2007»
    On Phase Transitions in Learning Sparse Networks
    13 years 10 months ago
    On Phase Transitions in Learning Sparse Networks
    Download  www.personeel.unimaas.nl
    In this paper we study the identification of sparse interaction networks as a machine learning problem. Sparsity means that we are provided with a small data set and a high number...
    Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ron...
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    KDD
    2008
    ACM
    259views Data Mining» more  KDD 2008»
    Using ghost edges for classification in sparsely labeled networks
    14 years 5 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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    JUCS
    2008
    109views more  JUCS 2008»
    Seamless Transition between Connected and Disconnected Collaborative Interaction
    13 years 4 months ago
    Seamless Transition between Connected and Disconnected Collaborative Interaction
    Download  www.jukm.org
    Abstract: Nowadays, more and more users make use of web-based collaborative systems. Users participate in communities or search for and provide information in webbased systems. The...
    Stephan Lukosch
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    ML
    2006
    ACM
    142views Machine Learning» more  ML 2006»
    The max-min hill-climbing Bayesian network structure learning algorithm
    13 years 4 months ago
    The max-min hill-climbing Bayesian network structure learning algorithm
    Download  www.dsl-lab.org
    We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
    Ioannis Tsamardinos, Laura E. Brown, Constantin F....
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    JITECH
    2010
    106views more  JITECH 2010»
    Patterns and structures of intra-organizational learning networks within a knowledge-intensive organization
    13 years 3 months ago
    Patterns and structures of intra-organizational learning networks within a knowledge-intensive organization
    Download  mihaskerlavaj.files.wordpress.com
    This paper employs the network perspective to study patterns and structures of intraorganizational learning networks. The theoretical background draws from cognitive theories, the...
    Miha Skerlavaj, Vlado Dimovski, Kevin C. Desouza
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