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In order for the model to adapt to the input as it changes its characteristics, a varying learning rate that does not merely depend on the training step but on the reconstruction error has been proposed. In the experiments, different configurations for classical competitive neural networks, self-organizing maps and growing neural gas with either per-neuron or per-network dynamic learning rate have been tested. Experimental results on document clustering tasks demonstrate the suitability of the proposal for real-world problems.<\/jats:p>","DOI":"10.3233\/ica-230701","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T11:30:38Z","timestamp":1676979038000},"page":"257-273","source":"Crossref","is-referenced-by-count":11,"title":["Dynamic learning rates for continual unsupervised learning"],"prefix":"10.1177","volume":"30","author":[{"given":"Jos\u00e9 David","family":"Fern\u00e1ndez-Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Department of Computer Languages and Computer Science, University of M\u00e1laga, M\u00e1laga, Spain"},{"name":"Biomedic Research Institute of M\u00e1laga (IBIMA), M\u00e1laga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esteban Jos\u00e9","family":"Palomo","sequence":"additional","affiliation":[{"name":"Department of Computer Languages and Computer Science, University of M\u00e1laga, M\u00e1laga, 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