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dc.contributor.authorAlvarez, V.
dc.contributor.authorMazuelas, S. 
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2022-07-18T19:53:06Z
dc.date.available2022-07-18T19:53:06Z
dc.date.issued2022-07
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1497
dc.description.abstractThe statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate of change by means of a carefully chosen learning rate, forgetting factor, or window size. However, the time changes in common scenarios are multidimensional, i.e., different statistical characteristics often change in a different manner. This paper presents adaptive minimax risk classifiers (AMRCs) that account for multidimensional time changes by means of a multivariate and high-order tracking of the time-varying underlying distribution. In addition, differently from conventional techniques, AMRCs can provide computable tight performance guarantees. Experiments on multiple benchmark datasets show the classification improvement of AMRCs compared to the state-of-the-art and the reliability of the presented performance guarantees.en_US
dc.description.sponsorshipRamon y Cajal Grant RYC-2016-19383 BCAM’s Severo Ochoa Excellence Accreditation SEV-2017-0718, the Spanish Ministry of Economic Affairs and Digital Transformation through Project IA4TES MIA.2021.M04.0008, and by the Basque Government through the IT-1504-22, and BERC 2022-2025 programmes.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.titleMinimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guaranteesen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publisherversionhttps://proceedings.mlr.press/v162/alvarez22a/alvarez22a.pdfen_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105058GA-I00en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleProceedings of the 39 th International Conference on Machine Learningen_US
dc.volume.number162en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España