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    NIPS
    2008
    211views Information Technology» more  NIPS 2008»
    Multi-Level Active Prediction of Useful Image Annotations for Recognition
    13 years 8 months ago
    Multi-Level Active Prediction of Useful Image Annotations for Recognition
    Download  www.cs.utexas.edu
    We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the categorylearner to strategic...
    Sudheendra Vijayanarasimhan, Kristen Grauman
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    ECML
    2006
    Springer
    223views Machine Learning» more  ECML 2006»
    Margin-Based Active Learning for Structured Output Spaces
    13 years 11 months ago
    Margin-Based Active Learning for Structured Output Spaces
    Download  l2r.cs.uiuc.edu
    In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
    Dan Roth, Kevin Small
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    DGO
    2008
    126views Education» more  DGO 2008»
    Active learning for e-rulemaking: public comment categorization
    13 years 8 months ago
    Active learning for e-rulemaking: public comment categorization
    Download  www.cs.cornell.edu
    We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...
    Stephen Purpura, Claire Cardie, Jesse Simons
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    BMCBI
    2010
    183views more  BMCBI 2010»
    Active learning for human protein-protein interaction prediction
    13 years 7 months ago
    Active learning for human protein-protein interaction prediction
    Download  www.cs.cmu.edu
    Background: Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is reso...
    Thahir P. Mohamed, Jaime G. Carbonell, Madhavi Gan...
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    KDD
    2009
    ACM
    180views Data Mining» more  KDD 2009»
    Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
    14 years 8 months ago
    Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
    Download  www.research.rutgers.edu
    Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
    Sofus A. Macskassy
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