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dc.contributor.authorŞahin, Ümitcanen_US
dc.contributor.authorYücesoy, V.en_US
dc.contributor.authorKoç, A.en_US
dc.contributor.authorTekin, Cemen_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2018-04-12T11:44:58Z
dc.date.available2018-04-12T11:44:58Z
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/11693/37592
dc.descriptionDate of Conference: 15-18 May 2017en_US
dc.descriptionConference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.description.abstractOptimum localization problem, which has a wide range of application areas in real life such as emergency services, command and control systems, warehouse localization, shipment planning, aims to find the best location to minimize the arrival, response or return time which might be vital in some applications. In most of the cases, uncertainty in traffic is the most challenging issue and in the literature generally it is assumed to obey a priori known stochastic distribution. In this study, problem is defined as the optimum localization of ambulances for emergency services and traffic is modeled to be Markovian to generate context data. Unlike the solution methods in the literature, there exists no mutual information transfer between the model and solution of the problem; thus, a contextual multi-armed bandit learner tries to determine the underlying traffic with simple assumptions. The performance of the bandit algorithm is compared with the performance of a classical estimation method in order to show the effectiveness of the learning approach on the solution of the optimum localization problem.en_US
dc.language.isoTurkishen_US
dc.source.titleProceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2017.7960447en_US
dc.subjectBest location detectionen_US
dc.subjectContextual bandit problemsen_US
dc.subjectEmergency medical systemsen_US
dc.subjectCommand and control systemsen_US
dc.subjectStochastic systemsen_US
dc.subjectTraffic signalsen_US
dc.subjectLocation detectionen_US
dc.subjectMulti armed banditen_US
dc.subjectMutual informationsen_US
dc.subjectStochastic distributionen_US
dc.subjectTraffic congestionen_US
dc.titleLearning traffic congestion by contextual bandit problems for optimum localizationen_US
dc.title.alternativeEn iyi konumlandırma için bağlamsal haydut problemleri ile trafik yoğunluğunu öğrenmeen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.identifier.doi10.1109/SIU.2017.7960447en_US
dc.publisherIEEEen_US


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