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 , used in multi-agent environments, proof that agents can communicate with each other ; 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.
|Title of host publication||2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|State||Published - 2 Feb 2018|
|Event||2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018 - Phu Quoc Island, Viet Nam|
Duration: 2 Feb 2018 → 4 Feb 2018
|Name||ACM International Conference Proceeding Series|
|Conference||2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018|
|City||Phu Quoc Island|
|Period||2/02/18 → 4/02/18|
Bibliographical notePublisher Copyright:
© Association for Computing Machinery. All rights reserved.
- Neural networks
- Reinforcement learning