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dc.contributor.authorLikas, A.en
dc.rightsDefault Licence-
dc.subjectvector quantizationen
dc.titleA reinforcement learning approach to online clusteringen
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.abstractA general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RC;CL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and significantly improve the performance of those algorithms, as indicated by experimental tests on well-known data sets.en
heal.journalNameNeural Computationen
heal.journalTypepeer reviewed-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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