{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:05:15Z","timestamp":1774541115124,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T00:00:00Z","timestamp":1681084800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T00:00:00Z","timestamp":1681084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12271479"],"award-info":[{"award-number":["12271479"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11248-7","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:13:32Z","timestamp":1681096412000},"page":"7009-7033","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Novel Regularization Paradigm for the Extreme Learning Machine"],"prefix":"10.1007","volume":"55","author":[{"given":"Yuao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yunwei","family":"Dai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2706-6264","authenticated-orcid":false,"given":"Qingbiao","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,10]]},"reference":[{"issue":"4","key":"11248_CR1","first-page":"431","volume":"30","author":"A Oussous","year":"2018","unstructured":"Oussous A, Benjelloun F-Z, Lahcen AA, Belfkih S (2018) Big data technologies: a survey. J King Saud Univer-Comp Inf Sci 30(4):431\u2013448","journal-title":"J King Saud Univer-Comp Inf Sci"},{"issue":"1","key":"11248_CR2","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s11063-017-9633-3","volume":"47","author":"R Rastogi","year":"2018","unstructured":"Rastogi R, Sharma S, Chandra S (2018) Robust parametric twin support vector machine for pattern classification. Neural Process Lett 47(1):293\u2013323","journal-title":"Neural Process Lett"},{"issue":"1","key":"11248_CR3","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s11063-020-10379-5","volume":"53","author":"NM Khan","year":"2021","unstructured":"Khan NM, Khan GM (2021) Real-time lossy audio signal reconstruction using novel sliding based multi-instance linear regression\/random forest and enhanced cgpann. Neural Process Lett 53(1):227\u2013255","journal-title":"Neural Process Lett"},{"issue":"2","key":"11248_CR4","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1007\/s11063-018-09976-2","volume":"50","author":"SK Pandey","year":"2019","unstructured":"Pandey SK, Janghel RR (2019) Recent deep learning techniques, challenges and its applications for medical healthcare system: a review. Neural Process Lett 50(2):1907\u20131935","journal-title":"Neural Process Lett"},{"issue":"7","key":"11248_CR5","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1007\/s00521-017-3223-1","volume":"31","author":"R Lent","year":"2019","unstructured":"Lent R (2019) A generalized reinforcement learning scheme for random neural networks. Neural Comp Appl 31(7):2699\u20132716","journal-title":"Neural Comp Appl"},{"issue":"6","key":"11248_CR6","doi-asserted-by":"publisher","first-page":"4886","DOI":"10.1109\/TCYB.2020.3022695","volume":"52","author":"S Ke","year":"2020","unstructured":"Ke S, Liu W (2020) Consistency of multiagent distributed generative adversarial networks. IEEE Trans Cybern 52(6):4886\u20134896","journal-title":"IEEE Trans Cybern"},{"key":"11248_CR7","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol. 2, IEEE pp. 985\u2013990"},{"issue":"1\u20133","key":"11248_CR8","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"11248_CR9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"G-B Huang","year":"2011","unstructured":"Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107\u2013122","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"11248_CR10","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1007\/s11063-021-10435-8","volume":"53","author":"S Zhu","year":"2021","unstructured":"Zhu S, Wang H, Lv H, Zhang H (2021) Augmented online sequential quaternion extreme learning machine. Neural Process Lett 53(2):1161\u20131186","journal-title":"Neural Process Lett"},{"issue":"8","key":"11248_CR11","doi-asserted-by":"publisher","first-page":"4201","DOI":"10.1007\/s00521-018-3346-z","volume":"31","author":"W Ibrahim","year":"2019","unstructured":"Ibrahim W, Abadeh MS (2019) Protein fold recognition using deep kernelized extreme learning machine and linear discriminant analysis. Neural Comp Appl 31(8):4201\u20134214","journal-title":"Neural Comp Appl"},{"issue":"3","key":"11248_CR12","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1007\/s11063-020-10282-z","volume":"52","author":"L Li","year":"2020","unstructured":"Li L, Zhao K, Sun R, Gan J, Yuan G, Liu T (2020) Parameter-free extreme learning machine for imbalanced classification. Neural Process Lett 52(3):1927\u20131944","journal-title":"Neural Process Lett"},{"issue":"2","key":"11248_CR13","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s11063-018-9919-0","volume":"50","author":"W Ren","year":"2019","unstructured":"Ren W, Han M (2019) Classification of EEG signals using hybrid feature extraction and ensemble extreme learning machine. Neural Process Lett 50(2):1281\u20131301","journal-title":"Neural Process Lett"},{"key":"11248_CR14","doi-asserted-by":"publisher","first-page":"105137","DOI":"10.1016\/j.compbiomed.2021.105137","volume":"141","author":"J Xia","year":"2022","unstructured":"Xia J, Yang D, Zhou H, Chen Y, Zhang H, Liu T, Heidari AA, Chen H, Pan Z (2022) Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm. Comp Biol Med 141:105137","journal-title":"Comp Biol Med"},{"issue":"6","key":"11248_CR15","doi-asserted-by":"publisher","first-page":"4643","DOI":"10.1007\/s00521-021-06619-x","volume":"34","author":"D El Bourakadi","year":"2022","unstructured":"El Bourakadi D, Yahyaouy A, Boumhidi J (2022) Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction. Neural Comput Appl 34(6):4643\u20134659","journal-title":"Neural Comput Appl"},{"issue":"2","key":"11248_CR16","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1007\/s11063-018-9888-3","volume":"50","author":"Y Li","year":"2019","unstructured":"Li Y, Zhang S, Yin Y, Zhang J, Xiao W (2019) A soft sensing scheme of gas utilization ratio prediction for blast furnace via improved extreme learning machine. Neural Process Lett 50(2):1191\u20131213","journal-title":"Neural Process Lett"},{"issue":"3","key":"11248_CR17","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1007\/s11063-016-9499-9","volume":"44","author":"Z Ma","year":"2016","unstructured":"Ma Z, Dai Q (2016) Selected an stacking elms for time series prediction. Neural Process Lett 44(3):831\u2013856","journal-title":"Neural Process Lett"},{"key":"11248_CR18","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.eswa.2018.12.024","volume":"121","author":"BS Raghuwanshi","year":"2019","unstructured":"Raghuwanshi BS, Shukla S (2019) Generalized class-specific kernelized extreme learning machine for multiclass imbalanced learning. Expert Sys Appl 121:244\u2013255","journal-title":"Expert Sys Appl"},{"issue":"11","key":"11248_CR19","doi-asserted-by":"publisher","first-page":"3363","DOI":"10.1007\/s00521-017-2922-y","volume":"30","author":"W Zou","year":"2018","unstructured":"Zou W, Yao F, Zhang B, Guan Z (2018) Improved meta-elm with error feedback incremental elm as hidden nodes. Neural Comp Appl 30(11):3363\u20133370","journal-title":"Neural Comp Appl"},{"issue":"3","key":"11248_CR20","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s11063-012-9246-9","volume":"37","author":"Y Yang","year":"2013","unstructured":"Yang Y, Wang Y, Yuan X (2013) Parallel chaos search based incremental extreme learning machine. Neural Process Lett 37(3):277\u2013301","journal-title":"Neural Process Lett"},{"key":"11248_CR21","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","volume":"61","author":"G Huang","year":"2015","unstructured":"Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32\u201348","journal-title":"Neural Netw"},{"issue":"2","key":"11248_CR22","first-page":"1200","volume":"7","author":"S Scardapane","year":"2017","unstructured":"Scardapane S, Wang D (2017) Randomness in neural networks: an overview. Wiley Interdiscipl Rev: Data Min Knowl Discovery 7(2):1200","journal-title":"Wiley Interdiscipl Rev: Data Min Knowl Discovery"},{"issue":"22","key":"11248_CR23","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: comparison from the usability perspective. Neural Comput Appl 33(22):15121\u201315144","journal-title":"Neural Comput Appl"},{"issue":"22","key":"11248_CR24","doi-asserted-by":"publisher","first-page":"16931","DOI":"10.1007\/s00521-020-04994-5","volume":"32","author":"AL Freire","year":"2020","unstructured":"Freire AL, Rocha-Neto AR, Barreto GA (2020) On robust randomized neural networks for regression: a comprehensive review and evaluation. Neural Comp Appl 32(22):16931\u201316950","journal-title":"Neural Comp Appl"},{"issue":"1","key":"11248_CR25","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10462-013-9405-z","volume":"44","author":"S Ding","year":"2015","unstructured":"Ding S, Zhao H, Zhang Y, Xu X, Nie R (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44(1):103\u2013115","journal-title":"Artif Intell Rev"},{"key":"11248_CR26","doi-asserted-by":"crossref","unstructured":"Zhang T, Deng Z, Choi K-S, Liu J, Wang S (2017) Robust extreme learning fuzzy systems using ridge regression for small and noisy datasets. In: 2017 IEEE International conference on fuzzy systems (FUZZ-IEEE), pp. 1\u20137","DOI":"10.1109\/FUZZ-IEEE.2017.8015417"},{"key":"11248_CR27","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2019.05.039","volume":"134","author":"H Yildirim","year":"2019","unstructured":"Yildirim H, \u00d6zkale MR (2019) The performance of elm based ridge regression via the regularization parameters. Expert Sys Appl 134:225\u2013233","journal-title":"Expert Sys Appl"},{"key":"11248_CR28","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neucom.2018.12.078","volume":"342","author":"T K\u00e4rkk\u00e4inen","year":"2019","unstructured":"K\u00e4rkk\u00e4inen T (2019) Extreme minimal learning machine: Ridge regression with distance-based basis. Neurocomputing 342:33\u201348","journal-title":"Neurocomputing"},{"issue":"5","key":"11248_CR29","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/83.382494","volume":"4","author":"MG Kang","year":"1995","unstructured":"Kang MG, Katsaggelos AK (1995) General choice of the regularization functional in regularized image restoration. IEEE Trans Image Process 4(5):594\u2013602","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"11248_CR30","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.1117\/1.601883","volume":"37","author":"MG Kang","year":"1998","unstructured":"Kang MG (1998) Generalized multichannel image deconvolution approach and its applications. Opt Eng 37(11):2953\u20132964","journal-title":"Opt Eng"},{"issue":"3","key":"11248_CR31","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1088\/0266-5611\/19\/3\/309","volume":"19","author":"E Haber","year":"2003","unstructured":"Haber E, Tenorio L (2003) Learning regularization functionals-a supervised training approach. Inver Prob 19(3):611","journal-title":"Inver Prob"},{"key":"11248_CR32","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.neucom.2017.07.018","volume":"275","author":"B Zhang","year":"2018","unstructured":"Zhang B, Ma Z, Liu Y, Yuan H, Sun L (2018) Ensemble based reactivated regularization extreme learning machine for classification. Neurocomputing 275:255\u2013266","journal-title":"Neurocomputing"},{"key":"11248_CR33","doi-asserted-by":"publisher","first-page":"105012","DOI":"10.1016\/j.knosys.2019.105012","volume":"188","author":"X-B Wang","year":"2020","unstructured":"Wang X-B, Zhang X, Li Z, Wu J (2020) Ensemble extreme learning machines for compound-fault diagnosis of rotating machinery. Knowl-Based Sys 188:105012","journal-title":"Knowl-Based Sys"},{"issue":"2","key":"11248_CR34","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1007\/s11063-019-10160-3","volume":"51","author":"G Li","year":"2020","unstructured":"Li G, Zou J (2020) Multi-parallel extreme learning machine with excitatory and inhibitory neurons for regression. Neural Process Lett 51(2):1579\u20131597","journal-title":"Neural Process Lett"},{"key":"11248_CR35","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1016\/j.neucom.2015.08.066","volume":"173","author":"Y Wang","year":"2016","unstructured":"Wang Y, Dou Y, Liu X, Lei Y (2016) Pr-elm: Parallel regularized extreme learning machine based on cluster. Neurocomputing 173:1073\u20131081","journal-title":"Neurocomputing"},{"issue":"6","key":"11248_CR36","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1109\/TNNLS.2017.2654357","volume":"29","author":"M Duan","year":"2017","unstructured":"Duan M, Li K, Liao X, Li K (2017) A parallel multiclassification algorithm for big data using an extreme learning machine. IEEE Trans Neural Netw Learn Syst 29(6):2337\u20132351","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11248_CR37","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.engappai.2019.03.011","volume":"81","author":"L Yao","year":"2019","unstructured":"Yao L, Ge Z (2019) Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data. Eng Appl Artif Intell 81:450\u2013465","journal-title":"Eng Appl Artif Intell"},{"key":"11248_CR38","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.cie.2019.02.024","volume":"130","author":"T Dokeroglu","year":"2019","unstructured":"Dokeroglu T, Sevinc E (2019) Evolutionary parallel extreme learning machines for the data classification problem. Comp Ind Eng 130:237\u2013249","journal-title":"Comp Ind Eng"},{"key":"11248_CR39","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536","journal-title":"Nature"},{"issue":"2","key":"11248_CR40","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1137\/0111030","volume":"11","author":"DW Marquardt","year":"1963","unstructured":"Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soci Ind Appl Math 11(2):431\u2013441","journal-title":"J Soci Ind Appl Math"},{"issue":"17","key":"11248_CR41","doi-asserted-by":"publisher","first-page":"3716","DOI":"10.1016\/j.neucom.2011.06.013","volume":"74","author":"JM Mart\u00ednez-Mart\u00ednez","year":"2011","unstructured":"Mart\u00ednez-Mart\u00ednez JM, Escandell-Montero P, Soria-Olivas E, Mart\u00edn-Guerrero JD, Magdalena-Benedito R, G\u00f3Mez-Sanchis J (2011) Regularized extreme learning machine for regression problems. Neurocomputing 74(17):3716\u20133721","journal-title":"Neurocomputing"},{"key":"11248_CR42","unstructured":"Dua D, Graff C (2019) UCI machine learning repository. https:\/\/archive.ics.uci.edu\/ml. Accessed 8 December 2021"},{"key":"11248_CR43","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1016\/j.neucom.2014.09.022","volume":"151","author":"K Zhang","year":"2015","unstructured":"Zhang K, Luo M (2015) Outlier-robust extreme learning machine for regression problems. Neurocomputing 151:1519\u20131527","journal-title":"Neurocomputing"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11248-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11248-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11248-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T19:10:38Z","timestamp":1698520238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11248-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,10]]},"references-count":43,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11248"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11248-7","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,10]]},"assertion":[{"value":"10 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2023","order":2,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}