Optimizing text quantifiers for multivariate loss functions (Articolo in rivista)

Type
Label
  • Optimizing text quantifiers for multivariate loss functions (Articolo in rivista) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Alternative label
  • Esuli A., Fabrizio S. (2015)
    Optimizing text quantifiers for multivariate loss functions
    in ERCIM news
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Esuli A., Fabrizio S. (literal)
Pagina inizio
  • 49 (literal)
Pagina fine
  • 49 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • ISSN: 0926-4981 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ercim-news.ercim.eu/images/stories/EN100/EN100-web.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 100 (literal)
Rivista
Note
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy ; Qatar Computing Research Institute (QCRI), Doha, Qatar (literal)
Titolo
  • Optimizing text quantifiers for multivariate loss functions (literal)
Abstract
  • Quantification - also known as class prior estimation - is the task of estimating the relative frequencies of classes in application scenarios in which such frequencies may change over time. This task is becoming increasingly important for the analysis of large and complex datasets. Researchers from ISTI-CNR, Pisa, are working with supervised learning methods explicitly devised with quantification in mind. (literal)
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