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hal.structure.identifier
dc.contributor.authorBen Hamida, Sana
HAL ID: 177299
ORCID: 0000-0003-4202-613X
*
hal.structure.identifier
dc.contributor.authorRukoz, Marta*
dc.date.accessioned2017-01-27T15:32:08Z
dc.date.available2017-01-27T15:32:08Z
dc.date.issued2016
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16215
dc.language.isoenen
dc.subjectTraining
dc.subjectTraining data
dc.subjectEvolutionary computation
dc.subjectSupervised learning
dc.subjectData mining
dc.subjectSociology
dc.subjectStatistics
dc.subject.ddc004; 005.7en
dc.titleTuning Active Sampling Techniques for Evolutionary Learner from Big Data Sets: Review and Discussion
dc.typeCommunication / Conférence
dc.description.abstractenBig data processing is the new challenge for analytical, machine learning techniques. Many efforts are needed to scale both classic, advanced methods to the the mass of the provided data. Evolutionary learning algorithms (EAL) are robust, effective methods in solving a wide variety of complex learning problems. This paper discusses how to tune the active sampling techniques for EAL to deal with very large training data sets. It introduces the key decisions needed to design an effective active sampling strategy, review the main techniques used with evolutionary algorithms. Then, we discuss how they could be adapted to learn from big training data sets, present some research directions in this domain.
dc.identifier.citationpages1206-1213
dc.relation.ispartoftitleUbiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 2016 Intl IEEE Conferences
dc.relation.ispartofeditorDidier El Baz, Julien Bourgeois
dc.relation.ispartofpublnameIEEE - Institute of Electrical and Electronics Engineers
dc.relation.ispartofpublcityPiscataway, NJ
dc.relation.ispartofdate2016
dc.subject.ddclabelInformatique générale; Organisation des donnéesen
dc.relation.ispartofisbn978-1-5090-2770-5
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0184
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2019-02-20T15:14:16Z
hal.identifierhal-01448255*
hal.version1*
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