A novel machine learning method based on generalized behavioral learning theory

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dc.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867
dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorTagluk, Mehmet Emin
dc.date.accessioned2024-08-04T20:41:41Z
dc.date.available2024-08-04T20:41:41Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description.abstractLearning is an important talent for understanding the nature and accordingly controlling behavioral characteristics. Behavioral learning theories are one of the popular learning theories which are built on experimental findings. These theories are widely applied in psychotherapy, psychology, neurology as well as in advertisements and robotics. There is an abundant literature associated with understanding learning mechanism, and various models have been proposed for the realization of learning theories. Nevertheless, none of those models are able to satisfactorily simulate the concept of classical conditioning. In this study, popular behavioral learning theories were firstly simplified and the contentious issues with them were clarified by conducting intuitive experiments. The experimental results and information available in the literature were evaluated, and behavioral learning theories were jointly generalized accordingly. The proposed model, to our knowledge, is the first one that possesses not only modeling all features of classical conditioning but also including all features with behavioral theories such as Pavlov, Watson, Guthrie, Thorndike and Skinner. Also, a microcontroller card (Arduino Mega 2560) was used to validate the applicability of the proposed model in robotics. Obtained results showed that this generalized model has a high capacity for modeling human learning. Then, the proposed learning model was further improved to be utilized as a machine learning method that can continuously learn similar to human being. The result obtained from the use of this method, in terms of computational cost and accuracy, showed that the proposed method can be successfully employed in machine learning, especially for time ordered datasets.en_US
dc.identifier.doi10.1007/s00521-016-2314-8
dc.identifier.endpage3939en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-84964067914en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3921en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-016-2314-8
dc.identifier.urihttps://hdl.handle.net/11616/97281
dc.identifier.volume28en_US
dc.identifier.wosWOS:000412842200020en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectEndless learningen_US
dc.subjectComputational modelen_US
dc.subjectClassificationen_US
dc.subjectRegressionen_US
dc.subjectBehavioral human learningen_US
dc.titleA novel machine learning method based on generalized behavioral learning theoryen_US
dc.typeArticleen_US

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