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Unsupervised fuzzy measure learning for classifier ensembles from coalitions performance
(IEEE, 2020)
info:eu-repo/semantics/article,
In Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: ...
A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models
(Elsevier, 2015)
Artículo / Artikulua,
This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point ...
A survey of fingerprint classification Part II: experimental analysis and ensemble proposal
(Elsevier, 2015)
Artículo / Artikulua,
In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the ...
A supervised fuzzy measure learning algorithm for combining classifiers
(Elsevier, 2023)
Artículo / Artikulua,
Fuzzy measure-based aggregations allow taking interactions among coalitions of the input sources into account. Their main drawback when applying them in real-world problems, such as combining classifier ensembles, is how ...