Reading PAGE

Peer Evaluation activity

Downloads 362

Total impact ?

    Send a

    Claudio has...

    Trusted 0
    Reviewed 0
    Emailed 0
    Shared/re-used 0
    Discussed 0
    Invited 0
    Collected 0

     

    This was brought to you by:

    block this user Claudio Sartori

    Professor

    Department of Electronics, Computer Science and Systems
    University of Bologna

    Two One-Pass Algorithms for Data Stream Classification using Approximate MEBs

    Export to Mendeley

    It has been recently shown that the quadratic programming formulation underlying a number of kernel methods can be treated as a minimal enclosing ball (MEB) problem in a feature space where data has been previously embedded. Core Vector Machines (CVMs) in particular, make use of this equivalence in order to compute SVM classifiers from very large datasets in the batch scenario. In this paper we study two al- gorithms for online classification which extend this family of algorithms to deal with large data streams. Both algorithms use analytical rules to adjust the model extracted from the stream instead of recomputing the entire solution on the augmented data-set. We show that these algo- rithms are more accurate than the current extension of CVMs to handle data streams using an analytical rule instead of solving large quadratic programs. Experiments also show that the online approaches are consid- erably more efficient than periodic computation of CVMs even though warm start is being used.

    Oh la laClose

    Your session has expired but don’t worry, your message
    has been saved.Please log in and we’ll bring you back
    to this page. You’ll just need to click “Send”.

    Your evaluation is of great value to our authors and readers. Many thanks for your time.

    Review Close

    Short review
    Select a comment
    Select a grade
    You and the author
    Anonymity My review is anonymous( Log in  or  Register )
    publish
    Close

    When you're done, click "publish"

    Only blue fields are mandatory.

    Relation to the author*
    Overall Comment*
    Anonymity* My review is anonymous( Log in  or  Register )
     

    Focus & Objectives*

    Have the objectives and the central topic been clearly introduced?

    Novelty & Originality*

    Do you consider this work to be an interesting contribution to knowledge?

    Arrangement, Transition and Logic

    Are the different sections of this work well arranged and distributed?

    Methodology & Results

    Is the author's methodology relevant to both the objectives and the results?

    Data Settings & Figures

    Were tables and figures appropriate and well conceived?

    References and bibliography

    Is this work well documented and has the bibliography been properly established?

    Writing

    Is this work well written, checked and edited?

    Write Your Review (you can paste text as well)
    Please be civil and constructive. Thank you.


    Grade (optional, N/A by default)

    N/A 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
    Close

    Your mailing list is currently empty.
    It will build up as you send messages
    and links to your peers.

     No one besides you has access to this list.
    Close
    Enter the e-mail addresses of your recipients in the box below.  Note: Peer Evaluation will NOT store these email addresses   log in
    Your recipients

    Your message:

    Your email : Your email address will not be stored or shared with others.

    Your message has been sent.

    Description

    Title : Two One-Pass Algorithms for Data Stream Classification using Approximate MEBs
    Author(s) : H ́ector Allende, Stefano Lodi, Ricardo Ǹanculef, and Claudio Sartori
    Abstract : It has been recently shown that the quadratic programming formulation underlying a number of kernel methods can be treated as a minimal enclosing ball (MEB) problem in a feature space where data has been previously embedded. Core Vector Machines (CVMs) in particular, make use of this equivalence in order to compute SVM classifiers from very large datasets in the batch scenario. In this paper we study two al- gorithms for online classification which extend this family of algorithms to deal with large data streams. Both algorithms use analytical rules to adjust the model extracted from the stream instead of recomputing the entire solution on the augmented data-set. We show that these algo- rithms are more accurate than the current extension of CVMs to handle data streams using an analytical rule instead of solving large quadratic programs. Experiments also show that the online approaches are consid- erably more efficient than periodic computation of CVMs even though warm start is being used.
    Keywords : data mining, machine learning, svm

    Subject : data mining, machine learning, svm
    Area : Computer Science
    Language : English
    Year : 2011

    Affiliations Department of Electronics, Computer Science and Systems
    University of Bologna
    Isbn : 978-3-642-20266-7

    Leave a comment

    This contribution has not been reviewed yet. review?

    You may receive the Trusted member label after :

    • Reviewing 10 uploads, whatever the media type.
    • Being trusted by 10 peers.
    • If you are blocked by 10 peers the "Trust label" will be suspended from your page. We encourage you to contact the administrator to contest the suspension.

    Does this seem fair to you? Please make your suggestions.

    Please select an affiliation to sign your evaluation:

    Cancel Evaluation Save

    Please select an affiliation:

    Cancel   Save

    Claudio's Peer Evaluation activity

    Claudio has...

    Trusted 0
    Reviewed 0
    Emailed 0
    Shared/re-used 0
    Discussed 0
    Invited 0
    Collected 0
    Invite this peer to...
    Title
    Start date (dd/mm/aaaa)
    Location
    URL
    Message
    send
    Close

    Full Text request

    Your request will be sent.

    Please enter your email address to be notified
    when this article becomes available

    Your email


     
    Your email address will not be shared or spammed.