Mañana, JUEVES, 24 DE ABRIL, el sistema se apagará debido a tareas habituales de mantenimiento a partir de las 9 de la mañana. Lamentamos las molestias.
Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning
dc.contributor.author | Cantador Gutiérrez, Iván | |
dc.contributor.author | Dorronsoro Ibero, José Ramón | |
dc.contributor.other | UAM. Departamento de Ingeniería Informática | es_ES |
dc.date.accessioned | 2015-03-18T18:01:03Z | |
dc.date.available | 2015-03-18T18:01:03Z | |
dc.date.issued | 2005 | |
dc.identifier.citation | Pattern Recognition and Image Analysis: Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II. Lecture Notes in Computer Science, Volumen 3523. Springer, 2005. 23-50. | es_ES |
dc.identifier.isbn | 978-3-540-26154-4 (print) | es_ES |
dc.identifier.isbn | 978-3-540-32238-2 (online) | es_ES |
dc.identifier.issn | 0302-9743 (print) | es_ES |
dc.identifier.issn | 1611-3349 (online) | es_ES |
dc.identifier.uri | http://hdl.handle.net/10486/664685 | |
dc.description | The final publication is available at Springer via http://dx.doi.org/10.1007/11492542_6 | es_ES |
dc.description | Proceedings of Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Part II | es_ES |
dc.description.abstract | A natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noisy patterns that belonging to one class are labelled as members of another. This allows classifier construction to focus on borderline patterns, likely to be the most informative ones. To appropriately define the above subsets, in this work we will use as base classifiers the so–called parallel perceptrons, a novel approach to committee machine training that allows, among other things, to naturally define margins for hidden unit activations. We shall use these margins to define the above pattern types and to iteratively perform subsample selections in an initial training set that enhance classification accuracy and allow for a balanced classifier performance even when class sizes are greatly different. | es_ES |
dc.description.sponsorship | With partial support of Spain’s CICyT, TIC 01–572, TIN2004–07676 | es_ES |
dc.format.extent | 9 pág. | es_ES |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer Berlin Heidelberg | es_ES |
dc.relation.ispartof | Lecture Notes in Computer Science | es_ES |
dc.rights | © Springer-Verlag Berlin Heidelberg 2005 | es_ES |
dc.subject.other | Computer Vision | es_ES |
dc.subject.other | Image Processing | es_ES |
dc.subject.other | Computer Graphics | es_ES |
dc.subject.other | Pattern Recognition | es_ES |
dc.title | Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning | es_ES |
dc.type | conferenceObject | es_ES |
dc.type | bookPart | en |
dc.subject.eciencia | Informática | es_ES |
dc.subject.eciencia | Telecomunicaciones | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1007/11492542_6 | es_ES |
dc.identifier.doi | 10.1007/11492542_6 | es_ES |
dc.identifier.publicationfirstpage | 43 | es_ES |
dc.identifier.publicationlastpage | 50 | es_ES |
dc.identifier.publicationvolume | 3523 | es_ES |
dc.relation.eventdate | June 7-9, 2005 | es_ES |
dc.relation.eventnumber | 2 | es_ES |
dc.relation.eventplace | Estoril (Portugal) | es_ES |
dc.relation.eventtitle | Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005 | es_ES |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es_ES |
dc.contributor.group | Neurocomputación Biológica (ING EPS-005) | es_ES |
dc.rights.accessRights | openAccess | es_ES |
dc.authorUAM | Cantador Gutiérrez, Iván (261086) | |
dc.authorUAM | Dorronsoro Ibero, José Ramón (259712) | |
dc.facultadUAM | Escuela Politécnica Superior |