V
Víctor Robles
Researcher at Technical University of Madrid
Publications - 61
Citations - 2491
Víctor Robles is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Estimation of distribution algorithm & Grid. The author has an hindex of 20, co-authored 61 publications receiving 2177 citations. Previous affiliations of Víctor Robles include Polytechnic University of Puerto Rico & Complutense University of Madrid.
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Journal ArticleDOI
Machine learning in bioinformatics
Pedro Larrañaga,Borja Calvo,Roberto Santana,Concha Bielza,Josu Galdiano,Iñaki Inza,Jose A. Lozano,Rubén Armañanzas,Guzmán Santafé,Aritz Pérez,Víctor Robles +10 more
TL;DR: Modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization, are presented.
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Feature selection for multi-label naive Bayes classification
TL;DR: This paper proposes a method called Mlnb which adapts the traditional naive Bayes classifiers to deal with multi-label instances and achieves comparable performance to other well-established multi- label learning algorithms.
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Age-Based Comparison of Human Dendritic Spine Structure Using Complete Three-Dimensional Reconstructions
TL;DR: This study assembled a large, quantitative database, which revealed a major reduction in spine densities in the aged case, suggesting selective alterations in spines with aging in humans and indicating that the spine volume and length are regulated by different biological mechanisms.
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A review of estimation of distribution algorithms in bioinformatics
Rubén Armañanzas,Iñaki Inza,Roberto Santana,Yvan Saeys,Jose Luis Flores,Jose A. Lozano,Yves Van de Peer,Rosa Blanco,Víctor Robles,Concha Bielza,Pedro Larrañaga +10 more
TL;DR: A basic taxonomy of EDA techniques is set out, underlining the nature and complexity of the probabilistic model of each EDA variant, and emphasizing the EDA paradigm's potential for further research in this domain.
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Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.
TL;DR: In this paper, the authors explored the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex.