{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:29:36Z","timestamp":1762507776692,"version":"3.37.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T00:00:00Z","timestamp":1559865600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T00:00:00Z","timestamp":1559865600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41872253","61603032"],"award-info":[{"award-number":["41872253","61603032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"crossref","award":["2016YFC0600510"],"award-info":[{"award-number":["2016YFC0600510"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"University of Science and Technology Beijing - National Taipei University of Technology Joint Research Program","award":["TW201705"],"award-info":[{"award-number":["TW201705"]}]},{"name":"Foundation from the National Taipei University of Technology of Taiwan","award":["NTUT-USTB-106-5"],"award-info":[{"award-number":["NTUT-USTB-106-5"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s13042-019-00967-w","type":"journal-article","created":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T06:02:54Z","timestamp":1559887374000},"page":"197-216","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A robust multilayer extreme learning machine using kernel risk-sensitive loss criterion"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1929-8447","authenticated-orcid":false,"given":"Xiong","family":"Luo","sequence":"first","affiliation":[]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[]},{"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaojuan","family":"Ban","sequence":"additional","affiliation":[]},{"given":"Jenq-Haur","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wenbing","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,7]]},"reference":[{"issue":"6","key":"967_CR1","first-page":"81","volume":"4","author":"SI Serengil","year":"2017","unstructured":"Serengil SI, Ozpinar A (2017) Workforce optimization for bank operation centers: a machine learning approach. Int J Interact Multimed Artif Intell 4(6):81\u201387","journal-title":"Int J Interact Multimed Artif Intell"},{"issue":"7","key":"967_CR2","first-page":"29","volume":"4","author":"C Elvira","year":"2018","unstructured":"Elvira C, Ochoa A, Gonzalvez JC, Moch\u00f3n F (2018) Machine-learning-based no show prediction in outpatient visits. Int J Interact Multimed Artif Intell 4(7):29\u201334","journal-title":"Int J Interact Multimed Artif Intell"},{"issue":"5","key":"967_CR3","first-page":"44","volume":"4","author":"AHH Alasadi","year":"2017","unstructured":"Alasadi AHH, Alsafy BM (2017) Diagnosis of malignant melanoma of skin cancer types. Int J Interact Multimed Artif Intell 4(5):44\u201349","journal-title":"Int J Interact Multimed Artif Intell"},{"issue":"1\u20133","key":"967_CR4","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"967_CR5","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"GB Huang","year":"2011","unstructured":"Huang GB, 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":"10","key":"967_CR6","doi-asserted-by":"crossref","first-page":"3254","DOI":"10.1109\/TCYB.2017.2648261","volume":"47","author":"C Cervellera","year":"2017","unstructured":"Cervellera C, Maccio D (2017) An extreme learning machine approach to density estimation problems. IEEE Trans Cybern 47(10):3254\u20133265","journal-title":"IEEE Trans Cybern"},{"key":"967_CR7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.patrec.2015.09.015","volume":"68","author":"A Iosifidis","year":"2015","unstructured":"Iosifidis A, Gabbouj M (2015) On the kernel extreme learning machine speedup. Pattern Recognit Lett 68:205\u2013210","journal-title":"Pattern Recognit Lett"},{"issue":"2","key":"967_CR8","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MCI.2015.2405316","volume":"10","author":"GB Huang","year":"2015","unstructured":"Huang GB, Bai Z, Kasun LLC, Vong CM (2015) Local receptive fields based extreme learning machine. IEEE Comput Intell Mag 10(2):18\u201329","journal-title":"IEEE Comput Intell Mag"},{"key":"967_CR9","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.neucom.2016.11.021","volume":"225","author":"H Zhu","year":"2016","unstructured":"Zhu H, Tsang ECC, Wang XZ (2016) Monotonic classification extreme learning machine. Neurocomputing 225:205\u2013213","journal-title":"Neurocomputing"},{"key":"967_CR10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.neunet.2016.06.001","volume":"81","author":"JW Cao","year":"2016","unstructured":"Cao JW, Zhang K, Luo MX, Yin C, Lai XP (2016) Extreme learning machine and adaptive sparse representation for image classification. Neural Netw 81:91\u2013102","journal-title":"Neural Netw"},{"issue":"3","key":"967_CR11","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s13042-016-0544-9","volume":"9","author":"X Luo","year":"2018","unstructured":"Luo X, Yang X, Jiang C, Ban XJ (2018) Timeliness online regularized extreme learning machine. Int J Mach Learn Cybern 9(3):465\u2013476","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"967_CR12","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10462-016-9461-2","volume":"46","author":"A Mozaffari","year":"2016","unstructured":"Mozaffari A, Azad NL (2016) Self-controlled bio-inspired extreme learning machines for scalable regression and classification: a comprehensive analysis with some recommendations. Artif Intell Rev 46(2):167\u2013223","journal-title":"Artif Intell Rev"},{"issue":"2","key":"967_CR13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s11063-015-9470-1","volume":"44","author":"JH Zhai","year":"2016","unstructured":"Zhai JH, Shao QY, Wang XZ (2016) Architecture selection of ELM networks based on sensitivity of hidden nodes. Neural Process Lett 44(2):471\u2013489","journal-title":"Neural Process Lett"},{"issue":"5","key":"967_CR14","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/s13042-014-0283-8","volume":"7","author":"S Balasundaram","year":"2016","unstructured":"Balasundaram S, Gupta D (2016) On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int J Mach Learn Cybern 7(5):707\u2013728","journal-title":"Int J Mach Learn Cybern"},{"key":"967_CR15","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.neucom.2016.11.021","volume":"225","author":"H Zhu","year":"2017","unstructured":"Zhu H, Tsang ECC, Wang XZ, Aamir Raza Ashfaq R (2017) Monotonic classification extreme learning machine. Neurocomputing 225:205\u2013213","journal-title":"Neurocomputing"},{"issue":"2","key":"967_CR16","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/s13042-015-0351-8","volume":"8","author":"S Ding","year":"2017","unstructured":"Ding S, Zhang N, Zhang J, Xu X, Shi Z (2017) Unsupervised extreme learning machine with representational features. Int J Mach Learn Cybern 8(2):587\u2013595","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"967_CR17","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s11063-016-9552-8","volume":"45","author":"MZ Alom","year":"2017","unstructured":"Alom MZ, Sidike P, Taha TM, Asari VK (2017) State preserving extreme learning machine: a monotonically increasing learning approach. Neural Process Lett 45(2):703\u2013725","journal-title":"Neural Process Lett"},{"key":"967_CR18","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/j.future.2018.04.085","volume":"93","author":"X Luo","year":"2019","unstructured":"Luo X, Jiang C, Wang W, Xu Y, Wang JH, Zhao W (2019) User behavior prediction in social networks using weighted extreme learning machine with distribution optimization. Future Gener Comput Syst 93:1023\u20131035","journal-title":"Future Gener Comput Syst"},{"issue":"10","key":"967_CR19","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TCYB.2014.2298235","volume":"44","author":"Z Bai","year":"2014","unstructured":"Bai Z, Huang GB, Wang D (2014) Sparse extreme learning machine for classification. IEEE Trans Cybern 44(10):1858\u20131870","journal-title":"IEEE Trans Cybern"},{"issue":"10","key":"967_CR20","doi-asserted-by":"crossref","first-page":"4528","DOI":"10.1016\/j.jfranklin.2015.07.002","volume":"352","author":"JW Cao","year":"2015","unstructured":"Cao JW, Zhao YF, Lai XP, Ong MEH, Yin C, Koh ZX, Liu N (2015) Landmark recognition with sparse representation classification and extreme learning machine. J Franklin Inst 352(10):4528\u20134545","journal-title":"J Franklin Inst"},{"key":"967_CR21","first-page":"1","volume":"5","author":"WP Cao","year":"2017","unstructured":"Cao WP, Ming Z, Wang XZ, Cai SB (2017) Improved bidirectional extreme learning machine based on enhanced random search. Memet Comput 5:1\u20138","journal-title":"Memet Comput"},{"key":"967_CR22","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.1016\/j.neucom.2009.02.013","volume":"72","author":"Y Lan","year":"2009","unstructured":"Lan Y, Soh YC, Huang GB (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72:3391\u20133395","journal-title":"Neurocomputing"},{"issue":"4","key":"967_CR23","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"JX Tang","year":"2016","unstructured":"Tang JX, Deng CW, Huang GB (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809\u2013821","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"967_CR24","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/TCYB.2014.2363492","volume":"45","author":"HM Zhou","year":"2015","unstructured":"Zhou HM, Huang GB, Lin ZP,\u00a0Wang H, Soh YC (2015) Stacked extreme learning machines. IEEE Trans Cybern 45(9):2013\u20132025","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"967_CR25","first-page":"127","volume":"14","author":"X Luo","year":"2017","unstructured":"Luo X, Deng J, Liu J, Wang W, Ban X, Wang JH (2017) A quantized kernel least mean square scheme with entropy-guided learning for intelligent data analysis. China Commun 14(7):127\u2013136","journal-title":"China Commun"},{"key":"967_CR26","unstructured":"Miche Y, Bas P, Jutten C,\u00a0Simula O, Lendasse A (2008) A methodology for building regression models using extreme learning machine: OP-ELM. In: Proc 16th Eur symposium artif neural netw\u2014adv comput intell learn, pp 247\u2013252"},{"issue":"4","key":"967_CR27","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/TIT.2005.844072","volume":"51","author":"D Guo","year":"2005","unstructured":"Guo D, Shamai S, Verdu S (2005) Mutual information and minimum mean-square error in Gaussian channels. IEEE Trans Inf Theory 51(4):1261\u20131282","journal-title":"IEEE Trans Inf Theory"},{"issue":"8","key":"967_CR28","first-page":"2368","volume":"48","author":"XJ Lu","year":"2017","unstructured":"Lu XJ, Ming L, Liu WB, Li HX (2017) Probabilistic regularized extreme learning machine for robust modeling of noise data. IEEE Trans Cybern 48(8):2368\u20132377","journal-title":"IEEE Trans Cybern"},{"key":"967_CR29","doi-asserted-by":"crossref","unstructured":"Zhang T, Deng ZH, Choi KS, Liu JF, Wang ST (2017) Robust extreme learning fuzzy systems using ridge regression for small and noisy datasets. In: Proc IEEE int conf fuzzy syst, pp 1\u20137","DOI":"10.1109\/FUZZ-IEEE.2017.8015417"},{"key":"967_CR30","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.neucom.2015.07.065","volume":"171","author":"SY Wong","year":"2016","unstructured":"Wong SY, Yap KS, Yap HJ (2016) A constrained optimization based extreme learning machine for noisy data regression. Neurocomputing 171:1431\u20131443","journal-title":"Neurocomputing"},{"issue":"61","key":"967_CR31","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1109\/TSP.2006.872524","volume":"54","author":"I Santamaria","year":"2006","unstructured":"Santamaria I, Pokharel PP, Principe JC (2006) Generalized correlation function: definition, properties, and application to blind equalization. IEEE Trans Signal Process 54(61):2187\u20132197","journal-title":"IEEE Trans Signal Process"},{"issue":"11","key":"967_CR32","doi-asserted-by":"crossref","first-page":"5286","DOI":"10.1109\/TSP.2007.896065","volume":"55","author":"W Liu","year":"2007","unstructured":"Liu W, Pokharel PP, Principe JC (2007) Correntropy: properties and applications in non-Gaussian signal processing. IEEE Trans Signal Process 55(11):5286\u20135298","journal-title":"IEEE Trans Signal Process"},{"issue":"8","key":"967_CR33","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2010.220","volume":"33","author":"R He","year":"2011","unstructured":"He R, Zheng WS, Hu BG (2011) Maximum correntropy criterion for robust face recognition. IEEE Trans Pattern Anal Mach Intell 33(8):1561\u20131576","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"967_CR34","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/LSP.2014.2319308","volume":"21","author":"BD Chen","year":"2014","unstructured":"Chen BD, Xing L, Liang JL,\u00a0Zheng N, Principe JC (2014) Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion. IEEE Signal Process Lett 21(7):880\u2013884","journal-title":"IEEE Signal Process Lett"},{"issue":"7\u20138","key":"967_CR35","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1007\/s00521-012-1184-y","volume":"23","author":"HJ Xing","year":"2013","unstructured":"Xing HJ, Wang XM (2013) Training extreme learning machine via regularized correntropy criterion. Neural Comput Appl 23(7\u20138):1977\u20131986","journal-title":"Neural Comput Appl"},{"issue":"11","key":"967_CR36","doi-asserted-by":"crossref","first-page":"4963","DOI":"10.1109\/TII.2018.2854549","volume":"14","author":"X Luo","year":"2018","unstructured":"Luo X, Sun J, Wang L, Wang W, Zhao W, Wu J, Wang JH, Zhang Z (2018) Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy. IEEE Trans Ind Inf 14(11):4963\u20134971","journal-title":"IEEE Trans Ind Inf"},{"issue":"4","key":"967_CR37","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1016\/j.jfranklin.2017.08.014","volume":"355","author":"X Luo","year":"2018","unstructured":"Luo X, Xu Y, Wang WP, Yuan MM, Ban XJ, Zhu YQ, Zhao WB (2018) Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy. J Franklin Inst 355(4):1945\u20131966","journal-title":"J Franklin Inst"},{"issue":"3","key":"967_CR38","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1007\/s11590-013-0626-5","volume":"8","author":"MN Syed","year":"2014","unstructured":"Syed MN, Pardalos PM, Principe JC (2014) On the optimization properties of the correntropic loss function in data analysis. Optim Lett 8(3):823\u2013839","journal-title":"Optim Lett"},{"issue":"11","key":"967_CR39","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1109\/TSP.2017.2669903","volume":"65","author":"BD Chen","year":"2017","unstructured":"Chen BD, Xing L, Xu B,\u00a0Zhao 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"},{"key":"967_CR40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.future.2015.10.022","volume":"61","author":"X Luo","year":"2016","unstructured":"Luo X, Zhang D, Yang LT, Liu J, Chang X, Ning H (2016) A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems. Future Gener Comput Syst 61:85\u201396","journal-title":"Future Gener Comput Syst"},{"key":"967_CR41","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4419-7683-3","volume-title":"Matrices: theory and applications","author":"D Serre","year":"2010","unstructured":"Serre D (2010) Matrices: theory and applications. Springer, New York"},{"key":"967_CR42","doi-asserted-by":"crossref","first-page":"239","DOI":"10.2307\/2284181","volume":"68","author":"PS Dwyer","year":"1973","unstructured":"Dwyer PS, Rao CR, Mitra SK (1973) Generalized inverse of matrices and its applications. J Am Stat Assoc 68:239","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"967_CR43","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2012","unstructured":"Huang GB, Zhou HM, Ding X,\u00a0Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513\u2013529","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"issue":"3","key":"967_CR44","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/1970392.1970395","volume":"58","author":"EJ Candes","year":"2011","unstructured":"Candes EJ, Li X, Ma Y (2011) Robust principal component analysis? J ACM 58(3):11","journal-title":"J ACM"},{"key":"967_CR45","volume-title":"Learning with kernels: support vector machines, regularization, optimization, and beyond","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge"},{"issue":"5","key":"967_CR46","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull JJ (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16(5):550\u2013554","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"967_CR47","unstructured":"Cole R, Fanty M (1994) UCI machine learning repository. \nhttps:\/\/archive.ics.uci.edu\/ml\/datasets\/ISOLET"},{"issue":"2","key":"967_CR48","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0933-3657(01)00082-3","volume":"23","author":"LA Kurgan","year":"2001","unstructured":"Kurgan LA, Cios KJ, Tadeusiewicz R, Ogiela M, Goodenday LS (2001) Knowledge discovery approach to automated cardiac SPECT diagnosis. Artif Intell Med 23(2):149\u2013169","journal-title":"Artif Intell Med"},{"key":"967_CR49","doi-asserted-by":"crossref","unstructured":"Kushmerick N (1999) Learning to remove Internet advertisements. In: Proc int conf autonom agents, pp 175\u2013181","DOI":"10.1145\/301136.301186"},{"key":"967_CR50","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0065-2407(08)60036-1","volume":"12","author":"D Klahr","year":"1978","unstructured":"Klahr D, Siegler RS (1978) The representation of children\u2019s knowledge. Adv Child Dev Behav 12:61\u2013116","journal-title":"Adv Child Dev Behav"},{"key":"967_CR51","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/978-3-319-55723-6_18","volume-title":"Data Science","author":"Elaine Fehrman","year":"2017","unstructured":"Fehrman E, Muhammad AK, Mirkes EM, Egan V, Gorban AN (2017) The five factor model of personality and evaluation of drug consumption risk. In: Palumbo F, Montanari A, Vichi M. (eds) Data science. Studies in classification, data analysis, and knowledge organization. Springer, Cham"},{"issue":"3","key":"967_CR52","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1023\/A:1007608224229","volume":"40","author":"TS Lim","year":"2000","unstructured":"Lim TS, Loh WY, Shih YS (2000) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn 40(3):203\u2013228","journal-title":"Mach Learn"},{"issue":"4","key":"967_CR53","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/JBHI.2013.2245674","volume":"17","author":"BE Sakar","year":"2013","unstructured":"Sakar BE, Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O (2013) Collection and Analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health 17(4):828\u2013834","journal-title":"IEEE J Biomed Health"},{"issue":"4","key":"967_CR54","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1021\/ci4000213","volume":"53","author":"K Mansouri","year":"2013","unstructured":"Mansouri K, Ringsted T, Ballabio D, Todeschini R, Consonni V (2013) Quantitative structure-activity relationship models for ready biodegradability of chemicals. J Chem Inf Model 53(4):867\u2013878","journal-title":"J Chem Inf Model"},{"issue":"1","key":"967_CR55","first-page":"91","volume":"55","author":"M Sikora","year":"2010","unstructured":"Sikora M, Wrobel L (2010) Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines. Arch Min Sci 55(1):91\u2013114","journal-title":"Arch Min Sci"},{"key":"967_CR56","unstructured":"Dua D, Karra TE (2019) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. \nhttp:\/\/archive.ics.uci.edu\/ml"},{"key":"967_CR57","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4419-1570-2","volume-title":"Information theoretic learning: Renyi\u2019s entropy and kernel perspectives","author":"JC Principe","year":"2010","unstructured":"Principe JC (2010) Information theoretic learning: Renyi\u2019s entropy and kernel perspectives. Springer, New York"},{"key":"967_CR58","volume-title":"System parameter identification: information criteria and algorithms","author":"BD Chen","year":"2013","unstructured":"Chen BD, Zhu Y, Hu JC, Principe JC (2013) System parameter identification: information criteria and algorithms. Elsevier, Amsterdam"},{"issue":"2","key":"967_CR59","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1109\/JIOT.2018.2856241","volume":"6","author":"M Chen","year":"2019","unstructured":"Chen M, Li Y, Luo X, Wang W, Wang L, Zhao W (2019) A novel human activity recognition scheme for smart health using multilayer extreme learning machine. IEEE Internet Things J 6(2):1410\u20131418","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"967_CR60","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s00521-015-1916-x","volume":"27","author":"LJ Chen","year":"2016","unstructured":"Chen LJ, Qu H, Zhao JH, Chen BD, Principe JC (2016) Efficient and robust deep learning with correntropy-induced loss function. Neural Comput Appl 27(4):1019\u20131031","journal-title":"Neural Comput Appl"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-00967-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-019-00967-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-00967-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T23:13:53Z","timestamp":1591398833000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-019-00967-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,7]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["967"],"URL":"https:\/\/doi.org\/10.1007\/s13042-019-00967-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2019,6,7]]},"assertion":[{"value":"23 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}