A novel machine learning method based on generalized behavioral learning theory


Ertugrul O. F. , TAĞLUK M. E.

NEURAL COMPUTING & APPLICATIONS, cilt.28, ss.3921-3939, 2017 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 28 Konu: 12
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s00521-016-2314-8
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Sayfa Sayıları: ss.3921-3939

Özet

Learning 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.