Quantification-oriented learning based on reliable classifiers

  title={Quantification-oriented learning based on reliable classifiers},
  author={Jos{\'e} Barranquero and Jorge D{\'i}ez and Juan Jos{\'e} del Coz},
  journal={Pattern Recognition},
Real-world applications demand effective methods to estimate the class distribution of a sample. In many domains, this is more productive than seeking individual predictions. At a first glance, the straightforward conclusion could be that this task, recently identified as quantification, is as simple as counting the predictions of a classifier. However, due to natural distribution changes occurring in real-world problems, this solution is unsatisfactory. Moreover, current quantification models… CONTINUE READING
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  • A. Esuli, F. Sebastiani
  • IEEE Intelligent Systems 25
  • 2010
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  • 2013
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