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dc.contributor.authorDurán-Rosal, Antonio Manuel
dc.contributor.authorDurán-Fernández, Aggeo
dc.contributor.authorFernández-Navarro, Francisco de Asís 
dc.contributor.authorCarbonero-Ruz, Mariano
dc.date.accessioned2023-05-04T06:56:26Z
dc.date.available2023-05-04T06:56:26Z
dc.date.created2023-05-03
dc.date.issued2022-12-07
dc.identifier.citationDurán-Rosal, A. M., Durán-Fernández, A., Fernández-Navarro, F., & Carbonero-Ruz, M. (2023). A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks. Applied Soft Computing, 133, 109914.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/26447
dc.description.abstractRandomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1-of- encoding ( the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks.es_ES
dc.description.sponsorshipFunding for open access charge: Universidad de Málaga / CBUAes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRedes neuronales (Informática)es_ES
dc.subjectKernel, Funciones dees_ES
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subject.otherExtreme Learning Machinees_ES
dc.subject.otherGeneralized eigenvalue problemes_ES
dc.subject.otherClassificationes_ES
dc.subject.otherKernel methodses_ES
dc.subject.otherRandomized-based feedforward neural networkses_ES
dc.subject.otherRandom vector functional link neural networkses_ES
dc.titleA multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2022.109914
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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