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dc.contributor.authorVillacampa-Calvo, Carlos 
dc.contributor.authorZaldívar, Bryan
dc.contributor.authorGarrido Merchán, Eduardo César 
dc.contributor.authorHernández Lobato, Daniel 
dc.contributor.otherUAM. Departamento de Ingeniería Informáticaes_ES
dc.date.accessioned2022-05-11T18:10:03Z
dc.date.available2022-05-11T18:10:03Z
dc.date.issued2021-01
dc.identifier.citationJournal Of Machine Learning Research 22.36 (2021): 1−52es_ES
dc.identifier.issn1532-4435 (print)es_ES
dc.identifier.issn1533-7928 (online)es_ES
dc.identifier.urihttp://hdl.handle.net/10486/701838
dc.description.abstractIt is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are common in real problems, as measurements are never perfectly accurate. If this input noise is not taken into account, a supervised machine learning method is expected to perform sub-optimally. In this paper, we focus on multi-class classification problems and use Gaussian processes (GPs) as the underlying classifier. Motivated by a data set coming from the astrophysics domain, we hypothesize that the observed data may contain noise in the inputs. Therefore, we devise several multi-class GP classifiers that can account for input noise. Such classifiers can be efficiently trained using variational inference to approximate the posterior distribution of the latent variables of the model. Moreover, in some situations, the amount of noise can be known before-hand. If this is the case, it can be readily introduced in the proposed methods. This prior information is expected to lead to better performance results. We have evaluated the proposed methods by carrying out several experiments, involving synthetic and real data. These include several data sets from the UCI repository, the MNIST data set and a data set coming from astrophysics. The results obtained show that, although the classification error is similar across methods, the predictive distribution of the proposed methods is better, in terms of the test log-likelihood, than the predictive distribution of a classifier based on GPs that ignores input noisees_ES
dc.description.sponsorshipWe would like to thank M. A. Sanchez-Conde, J. Coronado and V. Gammaldi for pointing our attention to the data set that motivated this work, as well as for the discussions concerning the data extraction. We thank as well E. Fernandez-Martınez, A. Suarez and C. M. Alaız-Gudin for useful discussions and feedback about the work. BZ especially acknowledges the hospitality of the Machine Learning group of UAM during the development of this project. BZ is supported by the Programa Atraccion de Talento de la Comunidad de Madrid under grant n. 2017-T2/TIC-5455, from the Spanish MINECO’s “Centro de Excelencia Severo Ochoa” Programme via grant SEV-2016-0597, and from the Comunidad de Madrid project SI1-PJI-2019-00294, of which BZ is the P.I. The authors gratefully acknowledge the use of the facilities of Centro de Computacion Cientıfica (CCC) at Universidad Autonoma de Madrid. The authors also acknowledge financial support from Spanish Plan Nacional I+D+i, grants TIN2016-76406-P. Finally, the authors acknowledge financial support from PID2019-106827GB-I00/AEI/10.13039/501100011033es_ES
dc.format.extent52 pag.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMicrotome Publishinges_ES
dc.relation.ispartofJournal of Machine Learning Researches_ES
dc.rights© The author(s)es_ES
dc.titleMulti-class Gaussian Process Classification with Noisy Inputses_ES
dc.typearticlees_ES
dc.subject.ecienciaInformáticaes_ES
dc.identifier.publicationfirstpage1es_ES
dc.identifier.publicationissue36es_ES
dc.identifier.publicationlastpage52es_ES
dc.identifier.publicationvolume22es_ES
dc.relation.projectID
dc.relation.projectIDGobierno de España. SEV-2016-0597es_ES
dc.relation.projectIDGobierno de España. TIN2016-76406-Pes_ES
dc.relation.projectIDGobierno de España. PID2019-106827GB-I00es_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.contributor.groupAprendizaje Automático (ING EPS-001)es_ES
dc.rights.ccReconocimientoes_ES
dc.rights.accessRightsopenAccesses_ES
dc.facultadUAMEscuela Politécnica Superiores_ES


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