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    » Tracking with general regression
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    SIGIR
    2008
    ACM
    167views Information Technology» more  SIGIR 2008»
    A bayesian logistic regression model for active relevance feedback
    13 years 9 months ago
    A bayesian logistic regression model for active relevance feedback
    Download  users.soe.ucsc.edu
    Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performanc...
    Zuobing Xu, Ram Akella
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    ICML
    1997
    IEEE
    155views Machine Learning» more  ICML 1997»
    Characterizing the generalization performance of model selection strategies
    14 years 10 months ago
    Characterizing the generalization performance of model selection strategies
    Download  www.cis.upenn.edu
    Abstract: We investigate the structure of model selection problems via the bias/variance decomposition. In particular, we characterize the essential structure of a model selection ...
    Dale Schuurmans, Lyle H. Ungar, Dean P. Foster
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    ISBI
    2008
    IEEE
    140views Medical Imaging» more  ISBI 2008»
    Template-based multiple hypotheses tracking of small vessels
    14 years 10 months ago
    Template-based multiple hypotheses tracking of small vessels
    Download  www.mevis-research.de
    A template tracking approach to the segmentation of small 3D vessel structures is presented. The main contributions are a general formulation of a vessel template function and a m...
    Ola Friman, Milo Hindennach, Heinz-Otto Peitgen
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    ECCV
    2008
    Springer
    162views Computer Vision» more  ECCV 2008»
    Learning to Localize Objects with Structured Output Regression
    14 years 11 months ago
    Learning to Localize Objects with Structured Output Regression
    Download  www.kyb.mpg.de
    Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to...
    Matthew B. Blaschko, Christoph H. Lampert
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    ICML
    2004
    IEEE
    136views Machine Learning» more  ICML 2004»
    Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
    14 years 10 months ago
    Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
    Download  www.cs.ust.hk
    Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
    Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
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