• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Maximum mutual information regularized classification

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    5_EngineeringAppOfAI.pdf
    Size:
    217.5Kb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Wang, Yi
    ZHAO, SHIGUANG
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2014-09-07
    Preprint Posting Date
    2014-09-27
    Online Publication Date
    2014-09-07
    Print Publication Date
    2015-01
    Permanent link to this record
    http://hdl.handle.net/10754/556641
    
    Metadata
    Show full item record
    Abstract
    In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.
    Citation
    Maximum mutual information regularized classification 2015, 37:1 Engineering Applications of Artificial Intelligence
    Publisher
    Elsevier BV
    Journal
    Engineering Applications of Artificial Intelligence
    DOI
    10.1016/j.engappai.2014.08.009
    arXiv
    1409.7780
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0952197614002085
    http://arxiv.org/abs/1409.7780
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.engappai.2014.08.009
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.