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Generalized Maximum Entropy for Supervised Classification 

Mazuelas, S.Autoridad BCAM; Shen, Y.; Pérez, A.Autoridad BCAM (2022-04)
The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ...
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Supervised non-parametric discretization based on Kernel density estimation 

Flores, J.L.; Calvo, B.; Pérez, A.Autoridad BCAM (2019-12-19)
Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main ...
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Crowd Learning with Candidate Labeling: an EM-based Solution 

Beñaran-Muñoz, I.Autoridad BCAM; Hernández-González, J.; Pérez, A.Autoridad BCAM (2018-09-27)
Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case ...

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Author
Pérez, A. (3)
Beñaran-Muñoz, I. (1)Calvo, B. (1)Flores, J.L. (1)Hernández-González, J. (1)Mazuelas, S. (1)Shen, Y. (1)Subject
Supervised classification (3)
Candidate labeling (1)Crowdsourced labels (1)Discretization (1)Expectation-maximization based method (1)generalized entropy (1)Kernel density estimation (1)maximum entropy (1)minimax risk classifiers (1)Non-parametric (1)... másFecha2022 (1)2019 (1)2018 (1)

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