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    KDD
    2008
    ACM
    137views Data Mining» more  KDD 2008»
    Learning classifiers from only positive and unlabeled data
    14 years 8 months ago
    Learning classifiers from only positive and unlabeled data
    Download  cseweb.ucsd.edu
    The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
    Charles Elkan, Keith Noto
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    KDD
    2008
    ACM
    259views Data Mining» more  KDD 2008»
    Using ghost edges for classification in sparsely labeled networks
    14 years 8 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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    KDD
    2008
    ACM
    174views Data Mining» more  KDD 2008»
    Automatic identification of quasi-experimental designs for discovering causal knowledge
    14 years 8 months ago
    Automatic identification of quasi-experimental designs for discovering causal knowledge
    Download  kdl.cs.umass.edu
    Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large databases. Quasi-experimental designs (QEDs) exploi...
    David D. Jensen, Andrew S. Fast, Brian J. Taylor, ...
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    KDD
    2007
    ACM
    165views Data Mining» more  KDD 2007»
    Finding low-entropy sets and trees from binary data
    14 years 8 months ago
    Finding low-entropy sets and trees from binary data
    Download  eprints.pascal-network.org
    The discovery of subsets with special properties from binary data has been one of the key themes in pattern discovery. Pattern classes such as frequent itemsets stress the co-occu...
    Eino Hinkkanen, Hannes Heikinheimo, Heikki Mannila...
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    KDD
    2006
    ACM
    201views Data Mining» more  KDD 2006»
    Clustering based large margin classification: a scalable approach using SOCP formulation
    14 years 8 months ago
    Clustering based large margin classification: a scalable approach using SOCP formulation
    Download  drona.csa.iisc.ernet.in
    This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture...
    J. Saketha Nath, Chiranjib Bhattacharyya, M. Naras...
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