Author:
González Pablo,Díez Jorge,Chawla Nitesh,del Coz Juan José
Funder
Ministerio de Economía y Competitividad
Publisher
Springer Science and Business Media LLC
Reference19 articles.
1. Barranquero, J., González, P., Díez, J., del Coz, J.J.: On the study of nearest neighbour algorithms for prevalence estimation in binary problems. Pattern Recognit. 46(2), 472–482 (2013)
2. Barranquero, J., Díez, J., del Coz, J.J.: Quantification-oriented learning based on reliable classifiers. Pattern Recognit. 48(2), 591–604 (2015)
3. Beijbom, O., Hoffman, J., Yao, E., Darrell, T., Rodriguez-Ramirez, A., Gonzalez-Rivero, M., Guldberg, O.H.: Quantification in-the-wild: data-sets and baselines. In: NIPS 2015, Workshop on Transfer and Multi-Task Learning. Montreal, CA (2015)
4. Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.: Quantification via probability estimators. In: Proc. of the 10th IEEE International Conference on Data Mining, pp. 737–742 (2010)
5. Esuli, A., Sebastiani, F.: Sentiment quantification. IEEE Intell. Syst. 25(4), 72–75 (2010)
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