Abstract
This paper presents a new neural algorithm, MS-SOM, as an extension of SOM, that maintaining the topological representation of stimulus also introduces a second level of organization of neurons. MS-SOM units tend to focus the learning process in data space zones with high values of a user-defined magnitude function. The model is based in two mechanisms: a secondary local competition step taking into account the magnitude of each unit, and the use of a learning factor, evaluated locally, for each unit. Some results in several examples demonstrate the better performance of MS-SOM compared to SOM.
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Pelayo, E., Buldain, D. (2014). MS-SOM: Magnitude Sensitive Self-Organizing Maps. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_3
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DOI: https://doi.org/10.1007/978-3-319-07695-9_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07694-2
Online ISBN: 978-3-319-07695-9
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