{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T08:10:05Z","timestamp":1756627805597,"version":"3.44.0"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100017142","name":"Gruppo Nazionale per il Calcolo Scientifico","doi-asserted-by":"publisher","award":["CUP-E53C23001670001"],"award-info":[{"award-number":["CUP-E53C23001670001"]}],"id":[{"id":"10.13039\/100017142","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s00521-025-11519-5","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T16:57:15Z","timestamp":1753808235000},"page":"20691-20719","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effective non-random extreme learning machine"],"prefix":"10.1007","volume":"37","author":[{"given":"Daniela","family":"De Canditiis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1563-4953","authenticated-orcid":false,"given":"Fabiano","family":"Veglianti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"issue":"2","key":"11519_CR1","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1002\/widm.1200","volume":"7","author":"S Scardapane","year":"2017","unstructured":"Scardapane S, Wang D (2017) Randomness in neural networks: an overview. WIREs Data Min Knowl Discov 7(2):1200. https:\/\/doi.org\/10.1002\/widm.1200","journal-title":"WIREs Data Min Knowl Discov"},{"key":"11519_CR2","doi-asserted-by":"publisher","unstructured":"Patil H, Sharma K (2023) Extreme learning machine: A comprehensive survey of theories and algorithms. In: 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), pp 749\u2013756. https:\/\/doi.org\/10.1109\/CISES58720.2023.10183613","DOI":"10.1109\/CISES58720.2023.10183613"},{"issue":"18","key":"11519_CR3","doi-asserted-by":"publisher","first-page":"10413","DOI":"10.1007\/s00521-024-09617-x","volume":"36","author":"MAA Albadr","year":"2024","unstructured":"Albadr MAA, AL-Dhief FT, Man L, Arram A, Abbas AH, Homod RZ (2024) Online sequential extreme learning machine approach for breast cancer diagnosis. Neural Comput Appl 36(18):10413\u201310429. https:\/\/doi.org\/10.1007\/s00521-024-09617-x","journal-title":"Neural Comput Appl"},{"issue":"12","key":"11519_CR4","doi-asserted-by":"publisher","first-page":"7909","DOI":"10.1007\/s00521-023-08992-1","volume":"37","author":"D Muduli","year":"2025","unstructured":"Muduli D, Kumar RR, Pradhan J, Kumar A (2025) An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection. Neural Comput Appl 37(12):7909\u20137924. https:\/\/doi.org\/10.1007\/s00521-023-08992-1","journal-title":"Neural Comput Appl"},{"issue":"20","key":"11519_CR5","doi-asserted-by":"publisher","first-page":"17329","DOI":"10.1007\/s00521-022-07395-y","volume":"34","author":"N Imik Tanyildizi","year":"2022","unstructured":"Imik Tanyildizi N, Tanyildizi H (2022) Estimation of voting behavior in election using support vector machine, extreme learning machine and deep learning. Neural Comput Appl 34(20):17329\u201317342. https:\/\/doi.org\/10.1007\/s00521-022-07395-y","journal-title":"Neural Comput Appl"},{"key":"11519_CR6","doi-asserted-by":"publisher","first-page":"15121","DOI":"10.1007\/s00521-021-06402-y","volume":"33","author":"U Markowska-Kaczmar","year":"2021","unstructured":"Markowska-Kaczmar U, Kosturek M (2021) Extreme learning machine versus classical feedforward network. Neural Comput Appl 33:15121\u201315144. https:\/\/doi.org\/10.1007\/s00521-021-06402-y","journal-title":"Neural Comput Appl"},{"issue":"10","key":"11519_CR7","doi-asserted-by":"publisher","first-page":"3466","DOI":"10.1109\/TCYB.2017.2734043","volume":"47","author":"D Wang","year":"2017","unstructured":"Wang D, Li M (2017) Stochastic configuration networks: fundamentals and algorithms. IEEE Trans Cybern 47(10):3466\u20133479. https:\/\/doi.org\/10.1109\/TCYB.2017.2734043","journal-title":"IEEE Trans Cybern"},{"issue":"16","key":"11519_CR8","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1016\/j.neucom.2010.11.030","volume":"74","author":"Y Wang","year":"2011","unstructured":"Wang Y, Cao F, Yuan Y (2011) A study on effectiveness of extreme learning machine. Neurocomputing 74(16):2483\u20132490. https:\/\/doi.org\/10.1016\/j.neucom.2010.11.030. (Advances in Extreme Learning Machine: Theory and Applications Biological Inspired Systems. Computational and Ambient Intelligence)","journal-title":"Neurocomputing"},{"issue":"12","key":"11519_CR9","doi-asserted-by":"publisher","first-page":"7733","DOI":"10.1007\/s00521-024-10578-4","volume":"37","author":"Q Ling","year":"2025","unstructured":"Ling Q, Tan K, Wang Y, Li Z, Liu W (2025) Evolutionary extreme learning machine based on an improved mopso algorithm. Neural Comput Appl 37(12):7733\u20137750. https:\/\/doi.org\/10.1007\/s00521-024-10578-4","journal-title":"Neural Comput Appl"},{"key":"11519_CR10","doi-asserted-by":"crossref","unstructured":"Hastie T, Tibshirani R, Friedman JH (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer series in statistics. Springer. https:\/\/books.google.it\/books?id=eBSgoAEACAAJ","DOI":"10.1007\/978-0-387-84858-7"},{"issue":"13","key":"11519_CR11","doi-asserted-by":"publisher","first-page":"3444","DOI":"10.1109\/TSP.2016.2546221","volume":"64","author":"R Giryes","year":"2016","unstructured":"Giryes R, Sapiro G, Bronstein AM (2016) Deep neural networks with random gaussian weights: a universal classification strategy? IEEE Trans Signal Process 64(13):3444\u20133457. https:\/\/doi.org\/10.1109\/TSP.2016.2546221","journal-title":"IEEE Trans Signal Process"},{"issue":"1","key":"11519_CR12","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ins.2011.09.015","volume":"185","author":"J Cao","year":"2012","unstructured":"Cao J, Lin Z, Huang G-B, Liu N (2012) Voting based extreme learning machine. Inf Sci 185(1):66\u201377. https:\/\/doi.org\/10.1016\/j.ins.2011.09.015","journal-title":"Inf Sci"},{"key":"11519_CR13","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-1-4612-0745-0_2","volume-title":"Priors for Infinite Networks","author":"RM Neal","year":"1996","unstructured":"Neal RM (1996) Priors for Infinite Networks. Springer, New York, pp 29\u201353. https:\/\/doi.org\/10.1007\/978-1-4612-0745-0_2"},{"issue":"6A","key":"11519_CR14","doi-asserted-by":"publisher","first-page":"4798","DOI":"10.1214\/23-AAP1933","volume":"33","author":"B Hanin","year":"2023","unstructured":"Hanin B (2023) Random neural networks in the infinite width limit as Gaussian processes. Ann Appl Probab 33(6A):4798\u20134819. https:\/\/doi.org\/10.1214\/23-AAP1933","journal-title":"Ann Appl Probab"},{"key":"11519_CR15","doi-asserted-by":"publisher","unstructured":"Favaro S, Hanin B, Marinucci D, Nourdin I, Peccati G (2023) Quantitative clts in deep neural networks. arXiv:2307.06092. https:\/\/doi.org\/10.48550\/arXiv.2307.06092","DOI":"10.48550\/arXiv.2307.06092"},{"key":"11519_CR16","doi-asserted-by":"publisher","unstructured":"Apollonio N., De\u00a0Canditiis D, Franzina G, Stolfi P, Torrisi GL Normal approximation of random gaussian neural networks. Stoch Syst. https:\/\/doi.org\/10.1287\/stsy.2023.0033","DOI":"10.1287\/stsy.2023.0033"},{"key":"11519_CR17","unstructured":"Han I, Zandieh A, Lee J, Novak R, Xiao L, Karbasi A (2024) Fast neural kernel embeddings for general activations. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. NIPS \u201922. Curran Associates Inc., Red Hook"},{"key":"11519_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.07.005","volume":"96","author":"W Zhang","year":"2019","unstructured":"Zhang W, Zhang Z, Wang L, Chao H-C, Zhou Z (2019) Extreme learning machines with expectation kernels. Pattern Recognit 96:106960. https:\/\/doi.org\/10.1016\/j.patcog.2019.07.005","journal-title":"Pattern Recognit"},{"key":"11519_CR19","unstructured":"Kelly M, Longjohn R, Nottingham K The UCI Machine Learning Repository. https:\/\/archive.ics.uci.edu"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11519-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11519-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11519-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T07:30:54Z","timestamp":1756625454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11519-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,29]]},"references-count":19,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["11519"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11519-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,7,29]]},"assertion":[{"value":"25 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}