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Artículo
Trainable and explainable simplicial map neural networks
dc.creator | Paluzo Hidalgo, Eduardo | es |
dc.creator | González Díaz, Rocío | es |
dc.creator | Gutiérrez Naranjo, Miguel Ángel | es |
dc.date.accessioned | 2024-04-23T07:41:07Z | |
dc.date.available | 2024-04-23T07:41:07Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Paluzo Hidalgo, E., González Díaz, R. y Gutiérrez Naranjo, M.Á. (2024). Trainable and explainable simplicial map neural networks. INFORMATION SCIENCES, 667, 120474. https://doi.org/10.1016/j.ins.2024.120474. | |
dc.identifier.issn | 1872-6291 | es |
dc.identifier.uri | https://hdl.handle.net/11441/156985 | |
dc.description.abstract | Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and no SMNN training process has been defined so far, so they lack generalization ability. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere. In addition, the explainability capacity of SMNNs and effective implementation are also newly introduced in this paper. | es |
dc.format | application/pdf | es |
dc.format.extent | 15 | es |
dc.language.iso | eng | es |
dc.publisher | ELSEVIER SCIENCE INC | es |
dc.relation.ispartof | INFORMATION SCIENCES, 667, 120474. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Explainable artificial intelligence | es |
dc.subject | Simplicial maps | es |
dc.subject | Training neural network | es |
dc.title | Trainable and explainable simplicial map neural networks | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) | es |
dc.identifier.doi | 10.1016/j.ins.2024.120474 | es |
dc.journaltitle | INFORMATION SCIENCES | es |
dc.publication.volumen | 667 | es |
dc.publication.initialPage | 120474 | es |
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15_Trainable_1-s2.0-S002002552 ... | 1.647Mb | [PDF] | Ver/ | |