Journal of Systems Integration, Vol 11, No 1 (2020)

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Learning in Neural Networks

Mavellas Sibanda

Abstract


This paper focuses on how artificial neural networks do their learning. This was done by going through an in-depth literature review focusing on the definition of learning, learning paradigms, learning rules and algorithms. A number of learning paradigms were identified and explained, which included supervised learning, unsupervised learning, hybrid learning and reinforcement learning. Various learning rules and their associated algorithms were also explained and illustrated, which includes Hebbian Learning Rule, Competitive Learning, Error Correction, Memory Based Learning and Boltzmann Learning.

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DOI: http://dx.doi.org/10.20470/jsi.v11i1.386

ISSN: 1804-2724

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This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 Czech Republic License.