Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/1363
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dc.contributor.authorPetrović, Andrijaen_US
dc.contributor.authorNikolić, Mladenen_US
dc.contributor.authorJovanović, Milošen_US
dc.contributor.authorDelibašić, Borisen_US
dc.date.accessioned2024-10-07T20:14:41Z-
dc.date.available2024-10-07T20:14:41Z-
dc.date.issued2023-02-01-
dc.identifier.issn09574174-
dc.identifier.urihttps://research.matf.bg.ac.rs/handle/123456789/1363-
dc.description.abstractGaussian conditional random fields (GCRF) are a well-known structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random field model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some appealing properties. Thanks to the GCRF latent structure, the model becomes tractable, efficient and open to improvements previously applied to GCRF regression models. In addition, the model allows for reduction of noise, that might appear if structures were defined directly between discrete outputs. Two different forms of the algorithm are presented: GCRFBCb (GCRGBC — Bayesian) and GCRFBCnb (GCRFBC — non-Bayesian). The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in Bayesian GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant. The inference in GCRFBCb is solved by Newton–Cotes formulas for one-dimensional integration. Both models are evaluated on synthetic data and real-world data. We show that both models achieve better prediction performance than unstructured predictors. Furthermore, computational and memory complexity is evaluated. Advantages and disadvantages of the proposed GCRFBCb and GCRFBCnb are discussed in detail.en_US
dc.language.isoenen_US
dc.publisherPergamon pressen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectDiscriminative graph-based modelen_US
dc.subjectEmpirical Bayesen_US
dc.subjectGaussian conditional random fieldsen_US
dc.subjectLocal variational approximationen_US
dc.subjectStructured classificationen_US
dc.titleGaussian conditional random fields for classificationen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2022.118728-
dc.identifier.scopus2-s2.0-85138167650-
dc.identifier.isi000875503900013-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85138167650-
dc.contributor.affiliationInformatics and Computer Scienceen_US
dc.relation.issn0957-4174en_US
dc.description.rankM21aen_US
dc.relation.firstpageArticle no. 118728en_US
dc.relation.volume212en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextembargo_20250301-
item.openairetypeArticle-
crisitem.author.deptInformatics and Computer Science-
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