Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Center for Advanced Study of Language
    • Center for Advanced Study of Language Research Works
    • View Item
    •   DRUM
    • Center for Advanced Study of Language
    • Center for Advanced Study of Language Research Works
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets

    Thumbnail
    View/Open
    activeLearningSupportVectorMachinesImbalancedDatasetsNAACL2009.pdf (253.6Kb)
    No. of downloads: 250

    Date
    2009-06
    Author
    Bloodgood, Michael
    Vijay-Shanker, K
    Citation
    Michael Bloodgood and K. Vijay-Shanker. 2009. Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, pages 137-140, Boulder, Colorado, June. Association for Computational Linguistics.
    DRUM DOI
    https://doi.org/10.13016/M2QP4K
    Metadata
    Show full item record
    Abstract
    Actively sampled data can have very different characteristics than passively sampled data. Therefore, it’s promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behind the proposed InitPA method for addressing imbalance is to base cost models during AL on an estimate of overall corpus imbalance computed via a small unbiased sample rather than the imbalance in the labeled training data, which is the leading method used during PL.
    URI
    http://hdl.handle.net/1903/15585
    Collections
    • Center for Advanced Study of Language Research Works

    Related items

    Showing items related by title, author, creator and subject.

    • Learning with Minimal Supervision: New Meta-Learning and Reinforcement Learning Algorithms 

      Sharaf, Amr (2020)
      Standard machine learning approaches thrive on learning from huge amounts of labeled training data, but what if we don’t have access to large amounts of labeled datasets? Humans have a remarkable ability to learn from only ...
    • The Protective Role of Home Learning Activities in the Development of Head Start Children's School Readiness Skills: A Longitudinal Analysis of Learning Growth Rates from Preschool Through First Grade 

      See, Heather M. (2008-11-17)
      Children's early learning experiences in the home have a significant impact on their readiness for school and future academic success. However, children in poverty often lack a high-quality home learning environment, and ...
    • THE ASSOCIATION OF CRITICAL THINKING AND PARTICIPATION IN LIVING AND LEARNING PROGRAMS: RESIDENTIAL HONORS COMPARED TO CIVIC/SOCIAL LEADERSHIP PROGRAMS AND NON-PARTICIPATION IN LIVING AND LEARNING PROGRAMS 

      Kohl, James Lucas (2009)
      This study explores the association of students' self-perceived critical thinking ability with participation in Residential Honors living-learning programs versus Civic/Social Leadership living-learning programs and ...

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility