Abstract
Deep Learning is a new area of Machine Learning research that deals with learning different levels of representation and abstraction in order to move Machine Learning closer to Artificial Intelligence. Artificial Metaplasticity are Artificial Learning Algorithms based on modelling higher level properties of biological plasticity: the plasticity of plasticity itself, so called Biological Metaplasticity. Artificial Metaplasticity aims to obtain general improvements in Machine Learning based on the experts generally accepted hypothesis that the Metaplasticity of neurons in Biological Brains is of high relevance in Biological Learning. This paper presents and discuss the results of applying different Artificial Metaplasticity implementations in Multilayer Perceptrons at artificial neuron learning level. To illustrate their potential, a relevant application that is the objective of state-of-the-art research has been chosen: the diagnosis of breast cancer data from the Wisconsin Breast Cancer Database. It then concludes that Artificial Metaplasticity also may play a high relevant role in Deep Learning.
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Fombellida, J., Torres-Alegre, S., Piñuela-Izquierdo, J.A., Andina, D. (2015). Artificial Metaplasticity for Deep Learning: Application to WBCD Breast Cancer Database Classification. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_42
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DOI: https://doi.org/10.1007/978-3-319-18833-1_42
Publisher Name: Springer, Cham
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