Abstract
We present our recent work on the Growing Hierarchical Self-Organizing Map, a dynamically growing neural network model which evolves into a hierarchical structure according to the necessities of the input data during an unsupervised training process. The benefits of this novel architecture are shown by organizing a real-world document collection according to semantic similarities.
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© 2001 Springer-Verlag London Limited
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Dittenbach, M., Rauber, A., Merkl, D. (2001). Recent Advances with the Growing Hierarchical Self-Organizing Map. In: Advances in Self-Organising Maps. Springer, London. https://doi.org/10.1007/978-1-4471-0715-6_20
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DOI: https://doi.org/10.1007/978-1-4471-0715-6_20
Publisher Name: Springer, London
Print ISBN: 978-1-85233-511-3
Online ISBN: 978-1-4471-0715-6
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