PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Sebastiani F. Text quantification. In: ECIR 2014 - Advances in Information Retrieval. 36th European Conference on Information Retrieval (Amsterdam, The Netherlands, 13-16 April 2014). Proceedings, pp. 819 - 822. Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofmann (eds.). (Lecture Notes in Computer Science, vol. 8416). Springer Verlag, 2014.
 
 
Abstract
(English)
In recent years it has been pointed out that, in a number of applications involving classification, the final goal is not determining which class (or classes) individual unlabelled data items belong to, but determining the prevalence (or “relative frequency”) of each class in the unlabelled data. The latter task has come to be known as quantification [1, 3, 5-10, 15, 18, 19].
URL: http://link.springer.com/chapter/10.1007/978-3-319-06028-6_104
DOI: 10.1007/978-3-319-06028-6_104
Subject Quantification
Prevalence estimation
Supervised learning
I.2.6 Learning


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