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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 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 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 ...
Motor-imagery-based brain-computer interface using signal derivation and aggregation functions
(IEEE, 2021)
info:eu-repo/semantics/article,
Brain Computer Interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to
BCI is Motor Imagery (MI). In BCI applications, the ...
Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
(IEEE, 2021)
info:eu-repo/semantics/article,
In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two ...
IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
(World Scientific Publishing Company, 2012)
Artículo / Artikulua,
The choice of membership functions plays an essential role in the success of fuzzy systems. This is a complex problem due to the possible lack of knowledge when assigning punctual values as membership degrees. To face this ...
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: ...