Learning More About Active Learning.

The article discusses improvements and innovation of the field of active learning algorithms that are creating large savings in label complexity. In an attempt to prevent junk email from reaching its intended recipients, early spam filters relied on a combination of brute force computing with passiv...

Descripción completa

Detalles Bibliográficos
Publicado en:Communications of the ACM Vol. 52; no. 4; pp. 11 - 14
Autor principal: Stemp-Morlock, Graeme
Formato: Artículo
Publicado: Association for Computing Machinery Apr2009
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=37295764&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 37295764
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00010782
        ACM
      jtl: Communications of the ACM
      issn: 00010782
      maglogo: N
    pubinfo:
      dt: Apr2009
      vid: 52
      iid: 4
      pid: 68
      pub: Association for Computing Machinery
    artinfo:
      ui:
        37295764
        10.1145/1498765.1498771
      ppf: 11
      ppct: 3
      formats:
      tig:
        atl: Learning More About Active Learning.
      aug:
        au: Stemp-Morlock, Graeme
      su:
        Spam filtering (Email)
        Spam email
        Algorithm research
        Filters (Mathematics)
        Email security
        Hanneke, Steve
        University faculty
        Students
      sug:
        subj:
          Spam filtering (Email)
          Spam email
          Algorithm research
          Filters (Mathematics)
          Email security
          Hanneke, Steve
          University faculty
          Students
      ab: The article discusses improvements and innovation of the field of active learning algorithms that are creating large savings in label complexity. In an attempt to prevent junk email from reaching its intended recipients, early spam filters relied on a combination of brute force computing with passive learning and refined processing with active learning, the article indicates. Commentary is provided by Ph.D. candidate Steve Hanneke on improvements made to active learning processes and informative examples as well as activized learning.
      pubtype: Periodical
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      dt:
        @attributes:
          year: 2009
    holdings:
      @attributes:
        islocal: N