{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:33:13Z","timestamp":1767652393937,"version":"3.41.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2017,4,27]],"date-time":"2017-04-27T00:00:00Z","timestamp":1493251200000},"content-version":"tdm","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":["11671010"],"award-info":[{"award-number":["11671010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Beijing Municipality (CN)","award":["4172035"],"award-info":[{"award-number":["4172035"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1007\/s00521-017-2966-z","type":"journal-article","created":{"date-parts":[[2017,4,27]],"date-time":"2017-04-27T03:59:44Z","timestamp":1493265584000},"page":"3799-3814","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Asymmetric \u03bd-twin support vector regression"],"prefix":"10.1007","volume":"30","author":[{"given":"Yitian","family":"Xu","sequence":"first","affiliation":[]},{"given":"Xiaoyan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xianli","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Zhiji","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,4,27]]},"reference":[{"key":"2966_CR1","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-77242-4","volume-title":"Support vector machines","author":"I Steinwart","year":"2008","unstructured":"Steinwart I, Christmann A (2008) Support vector machines. Springer, New York"},{"key":"2966_CR2","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1995","unstructured":"Vapnik V (1995) The nature of statistical learning theory. Springer, New York"},{"key":"2966_CR3","doi-asserted-by":"crossref","unstructured":"Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press","DOI":"10.1017\/CBO9780511809682"},{"key":"2966_CR4","doi-asserted-by":"crossref","unstructured":"Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press","DOI":"10.1017\/CBO9780511801389"},{"key":"2966_CR5","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B Sch\u00f6lkopf","year":"2000","unstructured":"Sch\u00f6lkopf B, Smola A, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12:1207\u20131245","journal-title":"Neural Comput"},{"key":"2966_CR6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/S0925-2312(03)00380-1","volume":"55","author":"J Bi","year":"2003","unstructured":"Bi J, Bennett KP (2003) A geometric approach to support vector regression. Neurocomputing 55:79\u2013108","journal-title":"Neurocomputing"},{"key":"2966_CR7","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 23:365\u2013372","journal-title":"Neural Netw"},{"key":"2966_CR8","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2011.09.021","volume":"79","author":"XJ Peng","year":"2012","unstructured":"Peng XJ (2012) Efficient twin parametric insensitive support vector regression model. Neurocomputing 79:26\u201338","journal-title":"Neurocomputing"},{"key":"2966_CR9","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.neucom.2014.02.028","volume":"138","author":"XJ Peng","year":"2014","unstructured":"Peng XJ, Xu D, Shen JD (2014) A twin projection support vector machine for data regression. Neurocomputing 138:131\u2013141","journal-title":"Neurocomputing"},{"key":"2966_CR10","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.sigpro.2008.10.002","volume":"89","author":"G Santanu","year":"2009","unstructured":"Santanu G, Mukherjee A, Dutta PK (2009) Nonparallel plane proximal classifier. Signal Process 89:510\u2013522","journal-title":"Signal Process"},{"key":"2966_CR11","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"KR Jayadeva","year":"2007","unstructured":"Jayadeva KR, Chandra S (2007) Khemchandani Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29:905\u2013910","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2966_CR12","first-page":"1063","volume":"9","author":"YT Xu","year":"2012","unstructured":"Xu YT, Xi WW, Lv X (2012) An improved least squares twin support vector machine. J Info Comput Sci 9:1063\u20131071","journal-title":"J Info Comput Sci"},{"key":"2966_CR13","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 S (2011) Chandra Reduced twin support vector regression. Neurocomputing 74:1474\u2013 1477","journal-title":"Neurocomputing"},{"key":"2966_CR14","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.neucom.2013.03.005","volume":"118","author":"YP Zhao","year":"2013","unstructured":"Zhao YP, Zhao J, Zhao M (2013) Twin least squares support vector regression. Neurocomputing 118:225\u2013236","journal-title":"Neurocomputing"},{"key":"2966_CR15","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 36:7535\u20137543","journal-title":"Expert Syst Appl"},{"key":"2966_CR16","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.eij.2014.12.003","volume":"16","author":"D Tomar","year":"2015","unstructured":"Tomar D, Agarwal S (2015) Twin Support Vector Machine: a review from 2007 to 2014. Egyptian Info J 16:55\u201369","journal-title":"Egyptian Info J"},{"key":"2966_CR17","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1109\/TPAMI.2013.178","volume":"36","author":"XL Huang","year":"2014","unstructured":"Huang XL, Shi L, Suykens JAK (2014) Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell 36:984\u2013997","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2966_CR18","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.csda.2014.03.016","volume":"77","author":"XL Huang","year":"2014","unstructured":"Huang XL, Shi L, Pelckmans K, Suykens JAK (2014) Asymmetric \u03bd-tube support vector regression. Comput Stat Data Anal 77:371\u2013382","journal-title":"Comput Stat Data Anal"},{"key":"2966_CR19","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.knosys.2015.12.005","volume":"95","author":"YT Xu","year":"2016","unstructured":"Xu YT, Yang ZJ, Zhang YQ, Pan XL, Wang LS (2016) A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification. Knowl-Based Syst 95:75\u201385","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"2966_CR20","first-page":"359","volume":"28","author":"YT Xu","year":"2016","unstructured":"Xu YT, Yang ZJ, Pan XL (2016) A novel twin support vector machine with pinball loss. IEEE Trans Neural Netw Learn Syst 28(2):359\u2013370","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2966_CR21","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neunet.2009.08.001","volume":"23","author":"PY Hao","year":"2010","unstructured":"Hao PY (2010) New support vector algorithms with parametric insensitive\/margin model. Neural Netw 23:60\u201373","journal-title":"Neural Netw"},{"key":"2966_CR22","doi-asserted-by":"crossref","first-page":"067002","DOI":"10.1103\/PhysRevLett.102.067002","volume":"102","author":"Q Le Masne","year":"2009","unstructured":"Le Masne Q, Pothier H, Birge NO, Urbina C, Esteve D (2009) Asymmetric noise probed with a josephson junction. Phys Rev Lett 102:067002","journal-title":"Phys Rev Lett"},{"key":"2966_CR23","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/S0167-7152(01)00124-9","volume":"54","author":"K Yu","year":"2001","unstructured":"Yu K, Moyeed RA (2001) Bayesian quantile regression. Stat Prob Lett 54:437\u2013447","journal-title":"Stat Prob Lett"},{"key":"2966_CR24","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/02664760701833388","volume":"35","author":"RN Sengupta","year":"2008","unstructured":"Sengupta RN (2008) Use of asymmetric loss functions in sequential estimation problems for multiple linear regression. J Appl Stat 35:245\u2013261","journal-title":"J Appl Stat"},{"key":"2966_CR25","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s10489-013-0500-2","volume":"41","author":"YT Xu","year":"2014","unstructured":"Xu YT, Guo R (2014) An improved \u03bd-twin support vector machine. Appl Intell 41:42\u201354","journal-title":"Appl Intell"},{"key":"2966_CR26","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1007\/s00521-011-0565-y","volume":"21","author":"YT Xu","year":"2012","unstructured":"Xu YT, Wang L, Zhong P (2012) A rough margin-based \u03bd-twin support vector machine. Neural Comput Applic 21:1307\u20131317","journal-title":"Neural Comput Applic"},{"key":"2966_CR27","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3150\/10-BEJ267","volume":"17","author":"I Steinwart","year":"2011","unstructured":"Steinwart I, Christmann A (2011) Estimating conditional quantiles with the help of the pinball loss. Bernoulli 17:211\u2013225","journal-title":"Bernoulli"},{"key":"2966_CR28","doi-asserted-by":"crossref","DOI":"10.1142\/5089","volume-title":"Least squares support vector machines","author":"JAK Suykens","year":"2002","unstructured":"Suykens JAK, Tony VG, Jos DB et al (2002) Least squares support vector machines. World Scientific Pub Co, Singapore"},{"key":"2966_CR29","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.spl.2011.11.007","volume":"82","author":"YT Xu","year":"2012","unstructured":"Xu YT (2012) A rough margin-based linear \u03bd support vector regression. Stat Prob Lett 82:528\u2013534","journal-title":"Stat Prob Lett"},{"key":"2966_CR30","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.knosys.2012.03.013","volume":"33","author":"YT Xu","year":"2012","unstructured":"Xu YT, Wang LS (2012) A weighted twin support vector regression. Knowl-Based Syst 33:92\u2013101","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"2966_CR31","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/72.950134","volume":"12","author":"F Navia-Vzquez","year":"2001","unstructured":"Navia-Vzquez F, Prez-Cruz A, Arts-Rodrguezand A, Figueiras-Vidal R (2001) Weighted least squares training of support vectors classifiers which leads to compact and adaptive schemes. IEEE Trans Neural Netw 12 (5):1047\u20131059","journal-title":"IEEE Trans Neural Netw"},{"key":"2966_CR32","unstructured":"Prez-Cruz J, Herrmann DJL, Scholkopf B (2003) Weston Weston Extension of the nu-SVM range for classification. In: Prez-Cruz J, Herrmann DJL, Scholkopf B (eds) Advances in learning theory: methods, models and applications. IOS Press, pp 179\u2013196"},{"issue":"5","key":"2966_CR33","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B Scholkopf","year":"2000","unstructured":"Scholkopf B, Smola A, Bartlett P, Williamson R (2000) New support vector algorithms. Neural Comput 12(5):1207\u20131245","journal-title":"Neural Comput"},{"key":"2966_CR34","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10489-014-0518-0","volume":"41","author":"YT Xu","year":"2014","unstructured":"Xu YT, Wang LS (2014) k-nearest neighbor-based weighted twin support vector regression. Appl Intell 41:299\u2013309","journal-title":"Appl Intell"},{"key":"2966_CR35","volume-title":"Nonparametric statistical inference","author":"JD Gibbons","year":"2011","unstructured":"Gibbons JD, Chakraborti S (2011) Nonparametric statistical inference, 5th Ed. Chapman & Hall CRC Press, Taylor & Francis Group, Boca Raton","edition":"5th Ed."},{"key":"2966_CR36","first-page":"1","volume":"7","author":"J Dems\u0306ar","year":"2006","unstructured":"Dems\u0306ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"2966_CR37","first-page":"2044","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining. Experimental analysis of power. Info Sci 180:2044\u20132064","journal-title":"Experimental analysis of power. Info Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-2966-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2966-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2966-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:42:56Z","timestamp":1750207376000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-2966-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,27]]},"references-count":37,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["2966"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-2966-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2017,4,27]]}}}