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    SDM
    2012
    SIAM
    252views Data Mining» more  SDM 2012»
    Learning from Heterogeneous Sources via Gradient Boosting Consensus
    11 years 11 months ago
    Learning from Heterogeneous Sources via Gradient Boosting Consensus
    Download  david.grangier.info
    Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
    Xiaoxiao Shi, Jean-François Paiement, David...
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    CGA
    1999
    150views Computational Geometry» more  CGA 1999»
    Visualizing Large Telecommunication Data Sets
    13 years 8 months ago
    Visualizing Large Telecommunication Data Sets
    Download  infovis.uni-konstanz.de
    displays to abstract network data and let users interactwithit.Wehaveimplementedafull-scaleSwift3D prototype, which generated the examples we present here. Swift-3D We developed Sw...
    Eleftherios Koutsofios, Stephen C. North, Daniel A...
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    SDM
    2003
    SIAM
    184views Data Mining» more  SDM 2003»
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    13 years 10 months ago
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    Download  www-users.cs.umn.edu
    The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
    Levent Ertöz, Michael Steinbach, Vipin Kumar
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    BIOSURVEILLANCE
    2008
    Springer
    107views Security Privacy» more  BIOSURVEILLANCE 2008»
    Dynamic Network Model for Predicting Occurrences of Salmonella at Food Facilities
    13 years 10 months ago
    Dynamic Network Model for Predicting Occurrences of Salmonella at Food Facilities
    Download  www.dbmi.pitt.edu
    Salmonella is among the most common food borne illnesses which may result from consumption of contaminated products. In this paper we model the co-occurrence data between USDA-cont...
    Purnamrita Sarkar, Lujie Chen, Artur Dubrawski
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    KDD
    2008
    ACM
    140views Data Mining» more  KDD 2008»
    Semi-supervised approach to rapid and reliable labeling of large data sets
    14 years 9 months ago
    Semi-supervised approach to rapid and reliable labeling of large data sets
    Download  www-users.cs.umn.edu
    Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
    György J. Simon, Vipin Kumar, Zhi-Li Zhang
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