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dc.contributor.authorZaidi, Nayyar Abbas-
dc.contributor.authorWebb, Geoffrey I.-
dc.contributor.authorCarman, Mark J.-
dc.contributor.authorPetitjean, François-
dc.contributor.authorCerquides, Jesús-
dc.date.accessioned2016-10-24T14:59:34Z-
dc.date.available2016-10-24T14:59:34Z-
dc.date.issued2016-
dc.identifierdoi: 10.1007/s10994-016-5574-8-
dc.identifierissn: 1573-0565-
dc.identifier.citationMachine Learning 104: 151- 194 (2016)-
dc.identifier.urihttp://hdl.handle.net/10261/139268-
dc.description.abstractThis paper introduces Accelerated Logistic Regression: a hybrid generative-discriminative approach to training Logistic Regression with high-order features. We present two main results: (1) that our combined generative-discriminative approach significantly improves the efficiency of Logistic Regression and (2) that incorporating higher order features (i.e. features that are the Cartesian products of the original features) reduces the bias of Logistic Regression, which in turn significantly reduces its error on large datasets. We assess the efficacy of Accelerated Logistic Regression by conducting an extensive set of experiments on 75 standard datasets. We demonstrate its competitiveness, particularly on large datasets, by comparing against state-of-the-art classifiers including Random Forest and Averaged n-Dependence Estimators.-
dc.description.sponsorshipThis research has been supported by the Australian Research Council (ARC) under grant DP140100087, and by the Asian Office of Aerospace Research and Development, Air Force Office of Scientific Research under contracts FA2386-15-1-4007 and FA2386-15-1-4017-
dc.publisherKluwer Academic Publishers-
dc.rightsclosedAccess-
dc.subjectGenerative-discriminative learning-
dc.subjectLow-bias classifiers-
dc.subjectHigher-order Logistic Regression-
dc.titleALR n: accelerated higher-order logistic regression-
dc.typeartículo-
dc.identifier.doi10.1007/s10994-016-5574-8-
dc.date.updated2016-10-24T14:59:34Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
dc.contributor.funderAustralian Research Council-
dc.contributor.funderAsian Office of Aerospace Research and Development-
dc.contributor.funderAir Force Office of Scientific Research (US)-
dc.relation.csic-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000923es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/100000181es_ES
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