PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Esuli A., Sebastiani F. Explicit loss minimization in quantification applications (Preliminary Draft). In: DART 2014 - 8th International Workshop on Information Filtering and Retrieval, co-located with XIII AI*IA Symposium on Artificial Intelligence (AI*IA 2014) (Pisa, 10 Dicembre 2014). Proceedings, pp. 1 - 11. Cristian Lai, Giovanni Semeraro, Alessandro Giuliani (eds.). (CEUR Workshop Proceedings, vol. 1314). CEUR-WS.org, 2014.
 
 
Abstract
(English)
In recent years there has been a growing interest in quantification, a variant of classification in which the final goal is not accurately classifying each unlabelled document but accurately estimating the prevalence (or "relative frequency") of each class c in the unlabelled set. Quantification has several applications in information retrieval, data mining, machine learning, and natural language processing, and is a dominant concern in fields such as market research, epidemiology, and the social sciences. This paper describes recent research in addressing quantification via explicit loss minimization, discussing works that have adopted this approach and some open questions that they raise.
URL: http://ceur-ws.org/Vol-1314/paper-01.pdf
Subject Text quantification
I.2.6 Learning


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