dc.creator | González Díaz, Rocío | es |
dc.creator | Gutiérrez Naranjo, Miguel Ángel | es |
dc.creator | Paluzo Hidalgo, Eduardo | es |
dc.date.accessioned | 2020-06-17T14:57:01Z | |
dc.date.available | 2020-06-17T14:57:01Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | González Díaz, R., Gutiérrez Naranjo, M.Á. y Paluzo Hidalgo, E. (2019). Two-hidden-layer Feedforward Neural Networks are Universal Approximators: A Constructive Approach. ArXiv.org, arXiv:1907.11457 | |
dc.identifier.uri | https://hdl.handle.net/11441/97963 | |
dc.description.abstract | It is well known that Artificial Neural Networks are universal approximators. The classical result
proves that, given a continuous function on a compact set on an n-dimensional space, then there exists
a one-hidden-layer feedforward network which approximates the function. Such result proves the
existence, but it does not provide a method for finding it. In this paper, a constructive approach to the
proof of this property is given for the case of two-hidden-layer feedforward networks. This approach
is based on an approximation of continuous functions by simplicial maps. Once a triangulation of
the space is given, a concrete architecture and set of weights can be obtained. The quality of the
approximation depends on the refinement of the covering of the space by simplicial complexes. | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Cornell University | es |
dc.relation.ispartof | ArXiv.org, arXiv:1907.11457 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Universal approximation theorem | es |
dc.subject | Simplicial approximation theorem | es |
dc.subject | Multilayer feedforward network | es |
dc.subject | Simplicial Complexes | es |
dc.title | Two-hidden-layer Feedforward Neural Networks are Universal Approximators: A Constructive Approach | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.publisherversion | https://arxiv.org/abs/1907.11457 | es |
dc.journaltitle | ArXiv.org | es |
dc.publication.issue | arXiv:1907.11457 | es |