A new approach for supervised learning based influence value reinforcement learning

André Valdivia, Jose Alfredo Herrera Quispe, Dennis Barrios-Aranibar

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The neural self-organization, is an innate feature of the mammal's brains, and is necessary for its operation. The most known neuronal models that use this characteristic are the self-organized maps (SOM) and the adaptive resonance theory (ART), but those models, did not take the neuron as a processing unit, as the biological counterpart. On the other hand, the influence value learning paradigm [1], used in multi-agent environments, proof that agents can communicate with each other [2]; and they can self-organize to assign tasks; without any interference. Motivated by this missing feature in artificial networks, and with the influence value reinforcement learning algorithm; a new approach to supervised learning was modeled using the neuron as an agent learning by reinforcement.

Idioma originalInglés
Título de la publicación alojada2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018
EditorialAssociation for Computing Machinery
Páginas24-28
Número de páginas5
ISBN (versión digital)9781450363365
DOI
EstadoPublicada - 2 feb. 2018
Evento2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018 - Phu Quoc Island, Vietnam
Duración: 2 feb. 20184 feb. 2018

Serie de la publicación

NombreACM International Conference Proceeding Series

Conferencia

Conferencia2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018
País/TerritorioVietnam
CiudadPhu Quoc Island
Período2/02/184/02/18

Nota bibliográfica

Publisher Copyright:
© Association for Computing Machinery. All rights reserved.

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