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 original | Inglés |
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Título de la publicación alojada | 2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018 |
Editorial | Association for Computing Machinery |
Páginas | 24-28 |
Número de páginas | 5 |
ISBN (versión digital) | 9781450363365 |
DOI | |
Estado | Publicada - 2 feb. 2018 |
Evento | 2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018 - Phu Quoc Island, Vietnam Duración: 2 feb. 2018 → 4 feb. 2018 |
Serie de la publicación
Nombre | ACM International Conference Proceeding Series |
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Conferencia
Conferencia | 2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018 |
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País/Territorio | Vietnam |
Ciudad | Phu Quoc Island |
Período | 2/02/18 → 4/02/18 |
Nota bibliográfica
Publisher Copyright:© Association for Computing Machinery. All rights reserved.