{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:06Z","timestamp":1740122646915,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872220"],"award-info":[{"award-number":["61872220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s10489-021-02852-y","type":"journal-article","created":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T05:02:47Z","timestamp":1635656567000},"page":"8572-8587","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Kernel risk-sensitive mean p-power loss based hyper-graph regularized robust extreme learning machine and its semi-supervised extension for sample classification"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5024-5530","authenticated-orcid":false,"given":"Zhen-Xin","family":"Niu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cui-Na","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang-Rui","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin-Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"issue":"6","key":"2852_CR1","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0893-6080(05)80131-5","volume":"6","author":"M Leshno","year":"1993","unstructured":"Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw 6(6):861\u2013867","journal-title":"Neural Netw"},{"issue":"6088","key":"2852_CR2","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(6088):533\u2013536","journal-title":"Nature"},{"issue":"3","key":"2852_CR3","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293\u2013300","journal-title":"Neural Process Lett"},{"key":"2852_CR4","unstructured":"Tong S, Koller D (2001) Support vector machine active learning with applications to text classification. J Mach Learn Res 2(Nov):45\u201366"},{"issue":"1\u20133","key":"2852_CR5","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":"4","key":"2852_CR6","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. Neural Netw 17(4):879\u2013892","journal-title":"Neural Netw"},{"key":"2852_CR7","first-page":"985","volume":"2","author":"G-B Huang","year":"2004","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw 2:985\u2013990","journal-title":"Neural Netw"},{"issue":"4","key":"2852_CR8","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"J Tang","year":"2015","unstructured":"Tang J, Deng C, Huang G-B (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809\u2013821","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"2852_CR9","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","volume":"6","author":"G-B Huang","year":"2014","unstructured":"Huang G-B (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6(3):376\u2013390","journal-title":"Cogn Comput"},{"issue":"1\u20133","key":"2852_CR10","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.neucom.2010.02.019","volume":"74","author":"G-B Huang","year":"2010","unstructured":"Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1\u20133):155\u2013163","journal-title":"Neurocomputing"},{"issue":"1","key":"2852_CR11","doi-asserted-by":"publisher","first-page":"197","DOI":"10.30526\/32.1.1978","volume":"32","author":"HM Salman","year":"2019","unstructured":"Salman HM (2019) Text Classification Based on Weighted Extreme Learning Machine. Ibn AL-Haitham J Pure Appl Sci 32(1):197\u2013204","journal-title":"Ibn AL-Haitham Journal For Pure and Applied Science"},{"key":"2852_CR12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.neucom.2016.04.069","volume":"261","author":"M Jiang","year":"2017","unstructured":"Jiang M, Pan Z, Li N (2017) Multi-label text categorization using L21-norm minimization extreme learning machine. Neurocomputing 261:4\u201310","journal-title":"Neurocomputing"},{"issue":"5","key":"2852_CR13","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TCYB.2018.2816981","volume":"49","author":"Y Chen","year":"2018","unstructured":"Chen Y, Song S, Li S, Yang L, Wu C (2018) Domain space transfer extreme learning machine for domain adaptation. IEEE Trans Cybern 49(5):1909\u20131922","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"2852_CR14","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1109\/TCYB.2016.2533424","volume":"47","author":"Z Huang","year":"2016","unstructured":"Huang Z, Yu Y, Gu J, Liu H (2016) An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans Cybern 47(4):920\u2013933","journal-title":"IEEE Transactions on Cybernetics"},{"key":"2852_CR15","first-page":"5235","volume":"5","author":"J Deng","year":"2017","unstructured":"Deng J, Fr\u00fchholz S, Zhang Z, Schuller B (2017) Recognizing emotions from whispered speech based on acoustic feature transfer learning. IEEE Access 5:5235\u20135246","journal-title":"IEEE Access"},{"key":"2852_CR16","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.eswa.2017.12.015","volume":"96","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Wang Y, Zhou G, Jin J, Wang B, Wang X, Cichocki A (2018) Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Syst Appl 96:302\u2013310","journal-title":"Expert Syst Appl"},{"issue":"01","key":"2852_CR17","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1142\/S0129065706000482","volume":"16","author":"N-Y Liang","year":"2006","unstructured":"Liang N-Y, Saratchandran P, Huang G-B, Sundararajan N (2006) Classification of mental tasks from EEG signals using extreme learning machine. Int J Neural Syst 16(01):29\u201338","journal-title":"Int J Neural Syst"},{"issue":"3","key":"2852_CR18","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1109\/TGRS.2019.2952236","volume":"58","author":"L Li","year":"2019","unstructured":"Li L, Zeng J, Jiao L, Liang P, Liu F, Yang S (2019) Online active extreme learning machine with discrepancy sampling for PolSAR classification. IEEE Trans Geosci Remote Sens 58(3):2027\u20132041","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2852_CR19","doi-asserted-by":"crossref","unstructured":"Samanta IS, Rout PK, Mishra S (2021) Feature extraction and power quality event classification using Curvelet transform and optimized extreme learning machine. Electr Eng 1\u201316","DOI":"10.1007\/s00202-021-01243-3"},{"issue":"12","key":"2852_CR20","doi-asserted-by":"publisher","first-page":"3567","DOI":"10.1109\/TCSII.2020.2990661","volume":"67","author":"W Lv","year":"2020","unstructured":"Lv W, Kang Y, Zheng WX, Wu Y, Li Z (2020) Feature-temporal semi-supervised extreme learning machine for robotic terrain classification. IEEE Trans Circuits Syst II Express Briefs 67(12):3567\u20133571","journal-title":"IEEE Trans Circuits Syst II Express Briefs"},{"issue":"12","key":"2852_CR21","doi-asserted-by":"publisher","first-page":"3397","DOI":"10.1007\/s13042-019-00926-5","volume":"10","author":"F Lv","year":"2019","unstructured":"Lv F, Han M (2019) Hyperspectral image classification based on multiple reduced kernel extreme learning machine. Int J Mach Learn Cybern 10(12):3397\u20133405","journal-title":"Int J Mach Learn Cybern"},{"key":"2852_CR22","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"},{"key":"2852_CR23","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neucom.2011.12.045","volume":"102","author":"P Horata","year":"2013","unstructured":"Horata P, Chiewchanwattana S, Sunat K (2013) Robust extreme learning machine. Neurocomputing 102:31\u201344","journal-title":"Neurocomputing"},{"key":"2852_CR24","doi-asserted-by":"crossref","unstructured":"Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513\u2013529","DOI":"10.1109\/TSMCB.2011.2168604"},{"key":"2852_CR25","doi-asserted-by":"publisher","first-page":"6575","DOI":"10.1109\/ACCESS.2018.2887260","volume":"7","author":"R Li","year":"2018","unstructured":"Li R, Wang X, Lei L, Song Y (2018) L2,1-norm based loss function and regularization extreme learning machine. IEEE Access 7:6575\u20136586","journal-title":"IEEE Access"},{"key":"2852_CR26","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.ast.2019.04.023","volume":"89","author":"Y-P Zhao","year":"2019","unstructured":"Zhao Y-P, Tan J-F, Wang J-J, Yang Z (2019) C-loss based extreme learning machine for estimating power of small-scale turbojet engine. Aerosp Sci Technol 89:407\u2013419","journal-title":"Aerosp Sci Technol"},{"issue":"6","key":"2852_CR27","doi-asserted-by":"publisher","first-page":"588","DOI":"10.3390\/e21060588","volume":"21","author":"T Zhang","year":"2019","unstructured":"Zhang T, Wang S, Zhang H, Xiong K, Wang L (2019) Kernel risk-sensitive mean p-power error algorithms for robust learning. Entropy 21(6):588","journal-title":"Entropy"},{"issue":"11","key":"2852_CR28","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.1109\/TSP.2017.2669903","volume":"65","author":"B Chen","year":"2017","unstructured":"Chen B, Xing L, Xu B, Zhao H, Zheng N, Principe JC (2017) Kernel risk-sensitive loss: definition, properties and application to robust adaptive filtering. IEEE Trans Signal Process 65(11):2888\u20132901","journal-title":"IEEE Trans Signal Process"},{"issue":"10","key":"2852_CR29","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1109\/JBHI.2020.2975199","volume":"24","author":"C-N Jiao","year":"2020","unstructured":"Jiao C-N, Gao Y-L, Yu N, Liu J-X, Qi L-Y (2020) Hyper-graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification. IEEE J Biomed Health Inform 24(10):3002\u20133011","journal-title":"IEEE J Biomed Health Inform"},{"issue":"8","key":"2852_CR30","first-page":"1548","volume":"33","author":"D Cai","year":"2010","unstructured":"Cai D, He X, Han J, Huang TS (2010) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548\u20131560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"2852_CR31","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1109\/TCYB.2014.2307349","volume":"44","author":"G Huang","year":"2014","unstructured":"Huang G, Song S, Gupta JN, Wu C (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405\u20132417","journal-title":"IEEE Transactions on Cybernetics"},{"key":"2852_CR32","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.knosys.2014.09.014","volume":"73","author":"X Luo","year":"2015","unstructured":"Luo X, Liu F, Yang S, Wang X, Zhou Z (2015) Joint sparse regularization based sparse semi-supervised extreme learning machine (S3ELM) for classification. Knowl Based Syst 73:149\u2013160","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"2852_CR33","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"N-Y Liang","year":"2006","unstructured":"Liang N-Y, Huang G-B, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411\u20131423","journal-title":"IEEE Trans Neural Networks"},{"issue":"7","key":"2852_CR34","doi-asserted-by":"publisher","first-page":"2101","DOI":"10.1109\/TCYB.2017.2727278","volume":"48","author":"B Chen","year":"2017","unstructured":"Chen B, Xing L, Wang X, Qin J, Zheng N (2017) Robust learning with kernel mean p-power error loss. IEEE Trans Cybern 48(7):2101\u20132113","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"1","key":"2852_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3325-0","volume":"21","author":"L-R Ren","year":"2020","unstructured":"Ren L-R, Gao Y-L, Liu J-X, Shang J, Zheng C-H (2020) Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification. BMC Bioinform 21(1):1\u201322","journal-title":"BMC Bioinformatics"},{"key":"2852_CR36","doi-asserted-by":"crossref","unstructured":"Yu N, Wu M-J, Liu J-X, Zheng C-H, Xu Y (2020) Correntropy-based hypergraph regularized NMF for clustering and feature selection on multi-cancer integrated data. IEEE Trans Cybern 51(8):1-12","DOI":"10.1109\/TCYB.2020.3000799"},{"issue":"2","key":"2852_CR37","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s12293-016-0198-x","volume":"9","author":"N Zhang","year":"2017","unstructured":"Zhang N, Ding S (2017) Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data. Memetic Comput 9(2):129\u2013139","journal-title":"Memetic Computing"},{"key":"2852_CR38","doi-asserted-by":"publisher","unstructured":"Ke J, Gong C, Liu T, Zhao L, Yang J, Tao D (2020) Laplacian Welsch regularization for robust semisupervised learning. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2019.2953337","DOI":"10.1109\/TCYB.2019.2953337"},{"issue":"5","key":"2852_CR39","doi-asserted-by":"publisher","first-page":"70","DOI":"10.3390\/sym9050070","volume":"9","author":"Q Shen","year":"2017","unstructured":"Shen Q, Ban X, Guo C (2017) Urban traffic congestion evaluation based on kernel the semi-supervised extreme learning machine. Symmetry 9(5):70","journal-title":"Symmetry"},{"key":"2852_CR40","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neunet.2019.09.030","volume":"122","author":"J Yang","year":"2020","unstructured":"Yang J, Cao J, Wang T, Xue A, Chen B (2020) Regularized correntropy criterion based semi-supervised ELM. Neural Netw 122:117\u2013129","journal-title":"Neural Netw"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02852-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02852-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02852-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T09:11:45Z","timestamp":1653901905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02852-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,31]]},"references-count":40,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["2852"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02852-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2021,10,31]]},"assertion":[{"value":"14 September 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"<b>The authors declare that they have no conflict of interest.<\/b>","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}