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We chose extreme learning as an example of randomisation-based networks. The models were evaluated in reference to training time and achieved efficiency. We conducted an extensive comparison of these two methods for various tasks in two scenarios:<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\bullet$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mo>\u2219<\/mml:mo><\/mml:math><\/jats:alternatives><\/jats:inline-formula>using comparable network capacity and<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\bullet$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mo>\u2219<\/mml:mo><\/mml:math><\/jats:alternatives><\/jats:inline-formula>using network architectures tuned for each model. The comparison was conducted on multiple datasets from public repositories and some artificial datasets created for this research. Overall, the experiments covered more than 50 datasets. Suitable statistical tests supported the results. They confirm that for relatively small datasets, extreme learning machines (ELM) are better than networks trained by the backpropagation algorithm. But for demanding image datasets, like ImageNet, ELM is not competitive to modern networks trained by backpropagation; therefore, in order to properly address current practical needs in pattern recognition entirely, ELM needs further development. Based on our experience, we postulate to develop smart algorithms for the inverse matrix calculation, so that determining weights for challenging datasets becomes feasible and memory efficient. There is a need to create specific mechanisms to avoid keeping the whole dataset in memory to compute weights. These are the most problematic elements in ELM processing, establishing the main obstacle in the widespread ELM application.<\/jats:p>","DOI":"10.1007\/s00521-021-06402-y","type":"journal-article","created":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T10:03:15Z","timestamp":1630317795000},"page":"15121-15144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Extreme learning machine versus classical feedforward network"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7606-3057","authenticated-orcid":false,"given":"Urszula","family":"Markowska-Kaczmar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2918-0688","authenticated-orcid":false,"given":"Micha\u0142","family":"Kosturek","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,30]]},"reference":[{"key":"6402_CR1","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.neunet.2018.01.005","volume":"99","author":"\u00c1 Arcos-Garc\u00eda","year":"2018","unstructured":"Arcos-Garc\u00eda \u00c1, \u00c1lvarez-Garc\u00eda JA, Soria-Morillo LM (2018) Deep neural network for traffic sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods. 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