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    SDM
    2008
    SIAM
    134views Data Mining» more  SDM 2008»
    Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
    15 years 8 months ago
    Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
    Download  sugiyama-www.cs.titech.ac.jp
    Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common...
    Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen...
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    SDM
    2008
    SIAM
    140views Data Mining» more  SDM 2008»
    Large-Scale Many-Class Learning
    15 years 8 months ago
    Large-Scale Many-Class Learning
    Download  www.personal.psu.edu
    In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We invest...
    Omid Madani, Michael Connor
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    SDM
    2008
    SIAM
    139views Data Mining» more  SDM 2008»
    Maximal Quasi-Bicliques with Balanced Noise Tolerance: Concepts and Co-clustering Applications
    15 years 8 months ago
    Maximal Quasi-Bicliques with Balanced Noise Tolerance: Concepts and Co-clustering Applications
    Download  www.comp.nus.edu.sg
    The rigid all-versus-all adjacency required by a maximal biclique for its two vertex sets is extremely vulnerable to missing data. In the past, several types of quasi-bicliques ha...
    Jinyan Li, Kelvin Sim, Guimei Liu, Limsoon Wong
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    SDM
    2007
    SIAM
    162views Data Mining» more  SDM 2007»
    Probabilistic Joint Feature Selection for Multi-task Learning
    15 years 8 months ago
    Probabilistic Joint Feature Selection for Multi-task Learning
    Download  siam.org
    We study the joint feature selection problem when learning multiple related classification or regression tasks. By imposing an automatic relevance determination prior on the hypo...
    Tao Xiong, Jinbo Bi, R. Bharat Rao, Vladimir Cherk...
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    SDM
    2007
    SIAM
    198views Data Mining» more  SDM 2007»
    Learning from Time-Changing Data with Adaptive Windowing
    15 years 8 months ago
    Learning from Time-Changing Data with Adaptive Windowing
    Download  siam.org
    We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
    Albert Bifet, Ricard Gavaldà
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