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    SDM
    2009
    SIAM
    225views Data Mining» more  SDM 2009»
    Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.
    16 years 4 months ago
    Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.
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    Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) ...
    Fei Wang, Chris H. Q. Ding, Tao Li
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    SDM
    2009
    SIAM
    204views Data Mining» more  SDM 2009»
    Application of Bayesian Partition Models in Warranty Data Analysis.
    16 years 4 months ago
    Application of Bayesian Partition Models in Warranty Data Analysis.
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    Automotive companies are forced to continuously extend and improve their product line-up. However, increasing diversity, higher design complexity, and shorter development cycles c...
    Axel Blumenstock, Christoph Schlieder, Markus M&uu...
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    SDM
    2009
    SIAM
    105views Data Mining» more  SDM 2009»
    Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
    16 years 4 months ago
    Exploiting Semantic Constraints for Estimating Supersenses with CRFs.
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    The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
    Gerhard Paaß, Frank Reichartz
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    SDM
    2009
    SIAM
    117views Data Mining» more  SDM 2009»
    Spatially Cost-Sensitive Active Learning.
    16 years 4 months ago
    Spatially Cost-Sensitive Active Learning.
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    In active learning, one attempts to maximize classifier performance for a given number of labeled training points by allowing the active learning algorithm to choose which points...
    Alexander Liu, Goo Jun, Joydeep Ghosh
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    SDM
    2009
    SIAM
    191views Data Mining» more  SDM 2009»
    Adaptive Concept Drift Detection.
    16 years 4 months ago
    Adaptive Concept Drift Detection.
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    An established method to detect concept drift in data streams is to perform statistical hypothesis testing on the multivariate data in the stream. Statistical decision theory off...
    Anton Dries, Ulrich Rückert
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