{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T05:05:26Z","timestamp":1754111126227,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"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":["61773182"],"award-info":[{"award-number":["61773182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00500-022-07755-9","type":"journal-article","created":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T15:24:28Z","timestamp":1673105068000},"page":"5357-5375","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Incremental learning for Lagrangian \u03b5-twin support vector regression"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7534-8333","authenticated-orcid":false,"given":"Binjie","family":"Gu","sequence":"first","affiliation":[]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Weili","family":"Xiong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"issue":"12","key":"7755_CR1","doi-asserted-by":"crossref","first-page":"8661","DOI":"10.1007\/s00521-019-04417-0","volume":"31","author":"M Ahmadi","year":"2019","unstructured":"Ahmadi M, Jafarzadeh-Ghoushchi S, Taghizadeh R, Sharifi A (2019) Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches. Neural Comput Appl 31(12):8661\u20138680","journal-title":"Neural Comput Appl"},{"key":"7755_CR2","doi-asserted-by":"crossref","unstructured":"Ahmadi M, Taghavirashidizadeh A, Javaheri D, Masoumian A, Jafarzadeh Ghoushchi S, Pourasad Y (2021) DQRE-SCnet: a novel hybrid approach for selecting users in federated learning with deep-Q-reinforcement learning based on spectral clustering. J King Saud Univ-Comput Inf Sci (in press)","DOI":"10.1016\/j.jksuci.2021.08.019"},{"key":"7755_CR3","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s11265-010-0514-5","volume":"65","author":"D Brugger","year":"2011","unstructured":"Brugger D, Rosenstiel W, Bogdan M (2011) Online SVR training by solving the primal optimization problem. J Signal Process Syst 65:391\u2013402","journal-title":"J Signal Process Syst"},{"issue":"2","key":"7755_CR4","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJC Burges","year":"1998","unstructured":"Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121\u2013167","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"7755_CR5","first-page":"553","volume":"15","author":"J Cao","year":"2021","unstructured":"Cao J, Gu BJ, Xiong WL, Pan F (2021) Incremental reduced least squares twin support vector regression. J Front Comput Sci Technol 15(3):553\u2013563","journal-title":"J Front Comput Sci Technol"},{"issue":"6","key":"7755_CR6","first-page":"1020","volume":"39","author":"J Cao","year":"2022","unstructured":"Cao J, Gu BJ, Pan F, Xiong WL (2022) Accurate incremental \u03b5-twin support vector regression. Control Theory Appl 39(6):1020\u20131032","journal-title":"Control Theory Appl"},{"key":"7755_CR7","unstructured":"Cauwenberghs G, Poggio T (2001) Incremental and decremental support vector machine learning. In: International conference on neural information processing systems. MIT Press"},{"issue":"9","key":"7755_CR8","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1162\/089976601750399335","volume":"13","author":"CC Chang","year":"2001","unstructured":"Chang CC, Lin CJ (2001) Training \u03bd-support vector classifiers: theory and algorithms. Neural Comput 13(9):2119\u20132147","journal-title":"Neural Comput"},{"issue":"3","key":"7755_CR9","doi-asserted-by":"crossref","first-page":"7435","DOI":"10.1007\/s10586-018-1772-4","volume":"22","author":"YT Chen","year":"2019","unstructured":"Chen YT, Xiong J, Xu WH, Zuo JW (2019) A novel online incremental and decremental learning algorithm based on variable support vector machine. Clust Comput 22(3):7435\u20137445","journal-title":"Clust Comput"},{"issue":"3","key":"7755_CR10","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"3","key":"7755_CR11","first-page":"551","volume":"7","author":"K Crammer","year":"2006","unstructured":"Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7(3):551\u2013558","journal-title":"J Mach Learn Res"},{"key":"7755_CR12","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini N, Shawe-Talyor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge"},{"issue":"12","key":"7755_CR13","first-page":"3146","volume":"28","author":"SF Ding","year":"2017","unstructured":"Ding SF, Huang HJ (2017) Least squares twin parametric insensitive support vector regression. J Softw 28(12):3146\u20133155","journal-title":"J Softw"},{"issue":"4","key":"7755_CR14","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama J, \u017dliobait\u0117 I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):44","journal-title":"ACM Comput Surv"},{"issue":"7","key":"7755_CR15","first-page":"194","volume":"55","author":"L Gan","year":"2019","unstructured":"Gan L, Yang M (2019) Pedestrian detection method based on ensemble SVM classifier. Comput Eng Appl 55(7):194\u2013198","journal-title":"Comput Eng Appl"},{"key":"7755_CR16","volume-title":"Matrix computations","author":"GH Golub","year":"1996","unstructured":"Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. John Hopkins University Press, Baltimore","edition":"3"},{"issue":"4","key":"7755_CR17","first-page":"466","volume":"33","author":"BJ Gu","year":"2016","unstructured":"Gu BJ, Pan F (2016) Accurate incremental online \u03bd-support vector regression learning algorithm. Control Theory Appl 33(4):466\u2013478","journal-title":"Control Theory Appl"},{"issue":"8","key":"7755_CR18","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1109\/TNNLS.2013.2250300","volume":"24","author":"B Gu","year":"2013","unstructured":"Gu B, Sheng VS (2013) Feasibility and finite convergence analysis for accurate on-line \u03bd-support vector machine. IEEE Trans Neural Netw Learn Syst 24(8):1304\u20131315","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7755_CR19","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neunet.2011.10.006","volume":"27","author":"B Gu","year":"2012","unstructured":"Gu B, Wang JD, Yu YC, Zheng GS, Huang YF et al (2012) Accurate on-line \u03bd-support vector learning. Neural Netw 27:51\u201359","journal-title":"Neural Netw"},{"key":"7755_CR20","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.neunet.2015.03.013","volume":"67","author":"B Gu","year":"2015","unstructured":"Gu B, Sheng VS, Wang Z, Ho D, Osman S et al (2015) Incremental learning for \u03bd-support vector regression. Neural Netw 67:140\u2013150","journal-title":"Neural Netw"},{"issue":"8","key":"7755_CR21","doi-asserted-by":"crossref","first-page":"6101","DOI":"10.1007\/s00500-020-04746-6","volume":"24","author":"BJ Gu","year":"2020","unstructured":"Gu BJ, Fang JW, Pan F, Bai ZH (2020) Fast clustering-based weighted twin support vector regression. Soft Comput 24(8):6101\u20136117","journal-title":"Soft Comput"},{"issue":"2","key":"7755_CR22","first-page":"230","volume":"43","author":"YH Hao","year":"2016","unstructured":"Hao YH, Zhang HF (2016) Incremental learning algorithm based on twin support vector regression. Comput Sci 43(2):230\u2013239","journal-title":"Comput Sci"},{"issue":"5","key":"7755_CR23","first-page":"502","volume":"33","author":"ZH Hu","year":"2015","unstructured":"Hu ZH, Xu YW, Zhao XL, He J, Zhou Y (2015) Multi-feature selection tracking based on support vector machine. J Appl Sci 33(5):502\u2013517","journal-title":"J Appl Sci"},{"issue":"21","key":"7755_CR24","doi-asserted-by":"crossref","first-page":"10649","DOI":"10.1007\/s00500-019-04002-6","volume":"23","author":"XP Hua","year":"2019","unstructured":"Hua XP, Xu S, Gao J, Ding SF (2019) L1-norm loss-based projection twin support vector machine for binary classification. Soft Comput 23(21):10649\u201310659","journal-title":"Soft Comput"},{"issue":"9","key":"7755_CR25","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1631\/jzus.CIIP1301","volume":"14","author":"HJ Huang","year":"2013","unstructured":"Huang HJ, Ding SF, Shi ZZ (2013) Primal least squares twin support vector regression. J Zhejiang Univ-Sci C-Comput Electron 14(9):722\u2013732","journal-title":"J Zhejiang Univ-Sci C-Comput Electron"},{"issue":"5","key":"7755_CR26","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"Jayadeva","year":"2007","unstructured":"Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905\u2013910","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"7755_CR27","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/TNN.2010.2048039","volume":"21","author":"M Karasuyama","year":"2010","unstructured":"Karasuyama M, Takeuchi I (2010) Multiple incremental decremental learning of support vector machines. IEEE Trans Neural Netw 21(7):1048\u20131059","journal-title":"IEEE Trans Neural Netw"},{"issue":"4","key":"7755_CR28","doi-asserted-by":"crossref","first-page":"7535","DOI":"10.1016\/j.eswa.2008.09.066","volume":"36","author":"MA Kumar","year":"2009","unstructured":"Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535\u20137543","journal-title":"Expert Syst Appl"},{"key":"7755_CR29","first-page":"1909","volume":"7","author":"P Laskov","year":"2006","unstructured":"Laskov P, Gehl C, Kr\u00fcger S, M\u00fcller KR (2006) Incremental support vector learning: analysis, implementation and application. J Mach Learn Res 7:1909\u20131936","journal-title":"J Mach Learn Res"},{"issue":"111","key":"7755_CR30","first-page":"1","volume":"18","author":"T Le","year":"2017","unstructured":"Le T, Nguyen TD, Nguyen V, Phung D (2017) Approximation vector machines for large-scale online learning. J Mach Learn Res 18(111):1\u201355","journal-title":"J Mach Learn Res"},{"key":"7755_CR31","doi-asserted-by":"crossref","unstructured":"Lilleberg J, Zhu Y, Zhang Y (2015) Support vector machines and word2vec for text classification with semantic features. In: International conference on cognitive informatics and cognitive computing. IEEE","DOI":"10.1109\/ICCI-CC.2015.7259377"},{"key":"7755_CR32","first-page":"1","volume":"17","author":"J Lu","year":"2016","unstructured":"Lu J, Steven CHH, Wang JL, Zhao PL, Liu ZY (2016) Large scale online kernel learning. J Mach Learn Res 17:1\u201343","journal-title":"J Mach Learn Res"},{"issue":"11","key":"7755_CR33","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1162\/089976603322385117","volume":"15","author":"JS Ma","year":"2003","unstructured":"Ma JS, Theiler J, Perkins S (2003) Accurate on-line support vector regression. Neural Comput 15(11):2683\u20132703","journal-title":"Neural Comput"},{"key":"7755_CR34","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.asoc.2018.02.040","volume":"66","author":"G Melki","year":"2018","unstructured":"Melki G, Kecman V, Ventura S, Cano A (2018) OLLAWV: online learning algorithm using worst-violators. Appl Soft Comput 66:384\u2013393","journal-title":"Appl Soft Comput"},{"issue":"17","key":"7755_CR35","doi-asserted-by":"crossref","first-page":"7725","DOI":"10.1007\/s00500-018-3397-1","volume":"23","author":"XY Pang","year":"2019","unstructured":"Pang XY, Xu YT (2019) A safe screening rule for accelerating weighted twin support vector machine. Soft Comput 23(17):7725\u20137739","journal-title":"Soft Comput"},{"issue":"3","key":"7755_CR36","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.neunet.2009.07.002","volume":"23","author":"XJ Peng","year":"2010","unstructured":"Peng XJ (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365\u2013372","journal-title":"Neural Netw"},{"issue":"2","key":"7755_CR37","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1137\/050624509","volume":"28","author":"HD Qi","year":"2006","unstructured":"Qi HD, Sun DF (2006) A quadratically convergent newton method for computing the nearest correlation matrix. SIAM J Matrix Anal Appl 28(2):360\u2013385","journal-title":"SIAM J Matrix Anal Appl"},{"issue":"3","key":"7755_CR38","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1007\/s10489-016-0860-5","volume":"46","author":"R Rastogi","year":"2017","unstructured":"Rastogi R, Anand P, Chandra S (2017) A \u03bd-twin support vector machine based on regression with automatic accuracy control. Appl Intell 46(3):670\u2013683","journal-title":"Appl Intell"},{"issue":"8","key":"7755_CR39","first-page":"2597","volume":"5","author":"JH Ruan","year":"2011","unstructured":"Ruan JH, Shi Y, Yang J (2011) Forest fires burned area prediction based on support vector machines with feature selection. ICIC Express Lett 5(8):2597\u20132603","journal-title":"ICIC Express Lett"},{"issue":"1","key":"7755_CR40","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-012-0924-3","volume":"23","author":"YH Shao","year":"2013","unstructured":"Shao YH, Zhang CH, Yang ZM, Ling J, Deng NY (2013) An \u03b5-twin support vector machine for regression. Neural Comput Appl 23(1):175\u2013185","journal-title":"Neural Comput Appl"},{"issue":"9","key":"7755_CR41","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1016\/j.neucom.2010.11.003","volume":"74","author":"M Singh","year":"2011","unstructured":"Singh M, Chadha J, Ahuja P, Jayadeva CS (2011) Reduced twin support vector regression. Neurocomputing 74(9):1474\u20131477","journal-title":"Neurocomputing"},{"issue":"3","key":"7755_CR42","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s13042-015-0361-6","volume":"8","author":"M Tanveer","year":"2017","unstructured":"Tanveer M, Shubham K (2017) A regularization on Lagrangian twin support vector regression. Int J Mach Learn Cybern 8(3):807\u2013821","journal-title":"Int J Mach Learn Cybern"},{"issue":"4","key":"7755_CR43","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s10489-015-0728-0","volume":"44","author":"M Tanveer","year":"2016","unstructured":"Tanveer M, Shubham K, Aldhaifallah M, Nisar KS (2016) An efficient implicit regularized Lagrangian twin support vector regression. Appl Intell 44(4):831\u2013848","journal-title":"Appl Intell"},{"key":"7755_CR44","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.asoc.2019.02.022","volume":"78","author":"M Tanveer","year":"2019","unstructured":"Tanveer M, Tiwari A, Choudhary R, Jalan S (2019) Sparse pinball twin support vector machines. Appl Soft Comput J 78:164\u2013175","journal-title":"Appl Soft Comput J"},{"issue":"5","key":"7755_CR45","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"VN Vapnik","year":"1999","unstructured":"Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988\u2013999","journal-title":"IEEE Trans Neural Netw"},{"issue":"4","key":"7755_CR46","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TNNLS.2013.2238556","volume":"24","author":"D Wang","year":"2013","unstructured":"Wang D, Qiao H, Zhang B, Wang M (2013) Online support vector machine based on convex hull vertices selection. IEEE Trans Neural Netw Learn Syst 24(4):593\u2013609","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"8","key":"7755_CR47","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1007\/s10489-019-01422-7","volume":"49","author":"LD Wang","year":"2019","unstructured":"Wang LD, Gao C, Zhao NN, Chen XB (2019) A projection wavelet weighted twin support vector regression and its primal solution. Appl Intell 49(8):3061\u20133081","journal-title":"Appl Intell"},{"issue":"2","key":"7755_CR48","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s10589-019-00075-z","volume":"73","author":"J Yin","year":"2019","unstructured":"Yin J, Li Q (2019) A semismooth Newton method for support vector classification and regression. Comput Optim Appl 73(2):477\u2013508","journal-title":"Comput Optim Appl"},{"issue":"3","key":"7755_CR49","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3724\/SP.J.1016.2008.00400","volume":"29","author":"HR Zhang","year":"2006","unstructured":"Zhang HR, Wang XD (2006) Incremental and online learning algorithm for regression least squares support vector machine. Chin J Comput 29(3):400\u2013406","journal-title":"Chin J Comput"},{"issue":"2","key":"7755_CR50","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s11063-017-9773-5","volume":"48","author":"ZQ Zhang","year":"2018","unstructured":"Zhang ZQ, Lv TL, Wang H, Liu LM, Tan JY (2018) A novel least square twin support vector regression. Neural Process Lett 48(2):1187\u20131200","journal-title":"Neural Process Lett"},{"key":"7755_CR51","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ins.2012.02.052","volume":"201","author":"YP Zhao","year":"2012","unstructured":"Zhao YP, Sun JG, Du ZH, Zhang ZA, Li YB (2012) Online independent reduced least squares support vector regression. Inf Sci 201:37\u201352","journal-title":"Inf Sci"},{"issue":"5","key":"7755_CR52","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1007\/s00521-011-0793-1","volume":"22","author":"J Zheng","year":"2013","unstructured":"Zheng J, Shen FR, Fan HJ, Zhao JX (2013) An incremental learning support vector machine for large-scale data. Neural Comput Appl 22(5):1023\u20131035","journal-title":"Neural Comput Appl"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-022-07755-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-022-07755-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-022-07755-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T12:12:34Z","timestamp":1681128754000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-022-07755-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,7]]},"references-count":52,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["7755"],"URL":"https:\/\/doi.org\/10.1007\/s00500-022-07755-9","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2023,1,7]]},"assertion":[{"value":"7 December 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 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":"All authors declare that there is no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper has not been previously published elsewhere, and it is not currently being considered for publication elsewhere.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}