Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAssis, Alex Damiany-
dc.contributor.authorTorres, Luiz Carlos Bambirra-
dc.contributor.authorAraújo, Lourenço Ribeiro Grossi-
dc.contributor.authorHanriot, Vítor Mourão-
dc.contributor.authorBraga, Antônio Pádua-
dc.identifier.citationASSIS, A. D. et al. Neural networks regularization with graph-based local resampling. IEEE Access, v. 9, p. 50727-50737, 2021. Disponível em: <>. Acesso em: 29 abr. 2022.pt_BR
dc.description.abstractThis paper presents the concept of Graph-based Local Resampling of perceptron-like neural networks with random projections (RN-ELM) which aims at regularization of the yielded model. The addition of synthetic noise to the learning set finds some similarity with data augmentation approaches that are currently adopted in many deep learning strategies. With the graph-based approach, however, it is possible to direct resample in the margin region instead of exhaustively cover the whole input space. The goal is to train neural networks with added noise in the margin region, located by structural information extracted from a planar graph. The so-called structural vectors, which are the training set vertices near the class boundary, are obtained from the structural information using Gabriel Graph. Synthetic samples are added to the learning set around the geometric vectors, improving generalization performance. A mathematical formulation that shows that the addition of synthetic samples has the same effect as the Tikhonov regularization is presented. Friedman and pos-hoc Nemenyi tests indicate that outcomes from the proposed method are statistically equivalent to the ones obtained by objective-function regularization, implying that both methods yield smoother solutions, reducing the effects of overfitting.pt_BR
dc.subjectTraining with noisept_BR
dc.titleNeural networks regularization with graph-based local resampling.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see Fonte: o PDF do artigo.pt_BR
Appears in Collections:DECSI - Artigos publicados em periódicos

Files in This Item:
File Description SizeFormat 
ARTIGO_NeuralNetworksRegularization.pdf1,95 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.