{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T03:26:19Z","timestamp":1768015579176,"version":"3.49.0"},"reference-count":147,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1007\/s00521-019-04627-6","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T16:26:24Z","timestamp":1575303984000},"page":"11173-11194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Robust statistics-based support vector machine and its variants: a survey"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6224-9707","authenticated-orcid":false,"given":"Manisha","family":"Singla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. K.","family":"Shukla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,2]]},"reference":[{"key":"4627_CR1","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.neucom.2012.11.023","volume":"110","author":"W An","year":"2013","unstructured":"An W, Liang M (2013) Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises. Neurocomputing 110:101\u2013110","journal-title":"Neurocomputing"},{"issue":"1\u20132","key":"4627_CR2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0925-2312(03)00435-1","volume":"55","author":"C Angulo","year":"2003","unstructured":"Angulo C, Parra X, Catala A (2003) K-SVCR a support vector machine for multi-class classification. Neurocomputing 55(1\u20132):57\u201377","journal-title":"Neurocomputing"},{"key":"4627_CR3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.knosys.2017.03.012","volume":"126","author":"SMH Bamakan","year":"2017","unstructured":"Bamakan SMH, Wang H, Shi Y (2017) Ramp loss k-support vector classification-regression; a robust and sparse multi-class approach to the intrusion detection problem. Knowl Based Syst 126:113\u2013126","journal-title":"Knowl Based Syst"},{"key":"4627_CR4","volume-title":"Outliers in statistical data","author":"V Barnett","year":"1974","unstructured":"Barnett V, Lewis T (1974) Outliers in statistical data. Wiley, Hoboken"},{"issue":"3","key":"4627_CR5","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/TFUZZ.2010.2042721","volume":"18","author":"R Batuwita","year":"2010","unstructured":"Batuwita R, Palade V (2010) Fsvm-cil: fuzzy support vector machines for class imbalance learning. IEEE Trans Fuzzy Syst 18(3):558\u2013571","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"Dec","key":"4627_CR6","first-page":"125","volume":"2","author":"A Ben-Hur","year":"2001","unstructured":"Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2(Dec):125\u2013137","journal-title":"J Mach Learn Res"},{"key":"4627_CR7","unstructured":"Bhukra MJ, Sharma KK (2018) Rician noise reduction with SVM, IMRD and iterative bilateral filter in different type of medical images using digital image processing"},{"issue":"1","key":"4627_CR8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.patcog.2008.07.004","volume":"42","author":"M Bicego","year":"2009","unstructured":"Bicego M, Figueiredo MA (2009) Soft clustering using weighted one-class support vector machines. Pattern Recognit 42(1):27\u201332","journal-title":"Pattern Recognit"},{"key":"4627_CR9","unstructured":"Biggio B, Nelson B, Laskov P (2011) Support vector machines under adversarial label noise. In: Asian conference on machine learning, pp 97\u2013112"},{"key":"4627_CR10","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.neucom.2017.01.012","volume":"235","author":"H Cevikalp","year":"2017","unstructured":"Cevikalp H, Franc V (2017) Large-scale robust transductive support vector machines. Neurocomputing 235:199\u2013209","journal-title":"Neurocomputing"},{"issue":"5","key":"4627_CR11","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1162\/neco.2007.19.5.1155","volume":"19","author":"O Chapelle","year":"2007","unstructured":"Chapelle O (2007) Training a support vector machine in the primal. Neural Comput 19(5):1155\u20131178","journal-title":"Neural Comput"},{"key":"4627_CR12","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.knosys.2017.06.009","volume":"131","author":"C Chen","year":"2017","unstructured":"Chen C, Li Y, Yan C, Guo J, Liu G (2017) Least absolute deviation-ased robust support vector regression. Knowl Based Syst 131:183\u2013194","journal-title":"Knowl Based Syst"},{"key":"4627_CR13","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.knosys.2015.09.025","volume":"90","author":"G Chen","year":"2015","unstructured":"Chen G, Zhang X, Wang ZJ, Li F (2015) Robust support vector data description for outlier detection with noise or uncertain data. Knowl Based Syst 90:129\u2013137","journal-title":"Knowl Based Syst"},{"key":"4627_CR14","doi-asserted-by":"crossref","unstructured":"Chen J, Ji G (2010) Weighted least squares twin support vector machines for pattern classification. In: 2010 The 2nd international conference on computer and automation engineering (ICCAE), IEEE, vol 2, pp 242\u2013246","DOI":"10.1109\/ICCAE.2010.5451483"},{"key":"4627_CR15","doi-asserted-by":"crossref","unstructured":"Chen X, Yang J, Liang J, Ye Q (2010) Robust and sparse twin support vector regression via linear programming. In: 2010 Chinese conference on pattern recognition (CCPR), IEEE, pp 1\u20136","DOI":"10.1109\/CCPR.2010.5659292"},{"issue":"12","key":"4627_CR16","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1007\/s00500-014-1342-5","volume":"18","author":"X Chen","year":"2014","unstructured":"Chen X, Yang J, Chen L (2014) An improved robust and sparse twin support vector regression via linear programming. Soft Comput 18(12):2335\u20132348","journal-title":"Soft Comput"},{"key":"4627_CR17","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang W, Zhang X (2018) Randomizing svm against adversarial attacks under uncertainty. In: Pacific-Asia conference on knowledge discovery and data mining, Springer, pp 556\u2013568","DOI":"10.1007\/978-3-319-93040-4_44"},{"issue":"13","key":"4627_CR18","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1016\/j.patrec.2009.05.007","volume":"30","author":"YS Choi","year":"2009","unstructured":"Choi YS (2009) Least squares one-class support vector machine. Pattern Recognit Lett 30(13):1236\u20131240","journal-title":"Pattern Recognit Lett"},{"issue":"3","key":"4627_CR19","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TSMCB.2006.889611","volume":"37","author":"CC Chuang","year":"2007","unstructured":"Chuang CC (2007) Fuzzy weighted support vector regression with a fuzzy partition. IEEE Trans Syst Man Cybern Part B (Cybern) 37(3):630\u2013640","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"issue":"1","key":"4627_CR20","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.asoc.2009.10.017","volume":"11","author":"CC Chuang","year":"2011","unstructured":"Chuang CC, Lee ZJ (2011) Hybrid robust support vector machines for regression with outliers. Appl Soft Comput 11(1):64\u201372","journal-title":"Appl Soft Comput"},{"issue":"6","key":"4627_CR21","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1109\/TNN.2002.804227","volume":"13","author":"CC Chuang","year":"2002","unstructured":"Chuang CC, Su SF, Jeng JT, Hsiao CC (2002) Robust support vector regression networks for function approximation with outliers. IEEE Trans Neural Netw 13(6):1322\u20131330","journal-title":"IEEE Trans Neural Netw"},{"key":"4627_CR22","doi-asserted-by":"crossref","unstructured":"Collobert R, Sinz F, Weston J, Bottou L (2006) Trading convexity for scalability. In: Proceedings of the 23rd international conference on Machine learning, ACM, pp 201\u2013208","DOI":"10.1145\/1143844.1143870"},{"issue":"3","key":"4627_CR23","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","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"},{"key":"4627_CR24","unstructured":"David M (2001) Tax one-class classification; concept-learning in the absence of counter-examples. ASCI dissertation series, 65"},{"key":"4627_CR25","doi-asserted-by":"crossref","unstructured":"Du H, Zhao S, Zhang D, Wu J (2016) Novel clustering-based approach for local outlier detection. In: 2016 IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, pp 802\u2013811","DOI":"10.1109\/INFCOMW.2016.7562187"},{"key":"4627_CR26","doi-asserted-by":"crossref","unstructured":"Dufrenois F, Noyer JC (2015) Generalized eigenvalue proximal support vector machines for outlier description. In: 2015 International joint conference on neural networks (IJCNN), IEEE, pp 1\u20139","DOI":"10.1109\/IJCNN.2015.7280343"},{"key":"4627_CR27","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","volume":"58","author":"SM Erfani","year":"2016","unstructured":"Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recognit 58:121\u2013134","journal-title":"Pattern Recognit"},{"key":"4627_CR28","doi-asserted-by":"crossref","unstructured":"Goel V, Raj H, Muthigi K, Kumar SS, Prasad D, Nath V (2019) Development of human detection system for security and military applications. In: Proceedings of the 3rd international conference on microelectronics, computing and communication systems, Springer, pp 195\u2013200","DOI":"10.1007\/978-981-13-7091-5_18"},{"issue":"1","key":"4627_CR29","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/10556780600883874","volume":"22","author":"MR Guarracino","year":"2007","unstructured":"Guarracino MR, Cifarelli C, Seref O, Pardalos PM (2007) A classification method based on generalized eigenvalue problems. Optim Methods Softw 22(1):73\u201381","journal-title":"Optim Methods Softw"},{"key":"4627_CR30","doi-asserted-by":"crossref","unstructured":"Gupta G, Ghosh J (2005) Robust one-class clustering using hybrid global and local search. In: Proceedings of the 22nd international conference on machine learning, ACM, pp 273\u2013280","DOI":"10.1145\/1102351.1102386"},{"key":"4627_CR31","doi-asserted-by":"crossref","unstructured":"Gurumurthy S, Sushama C, Ramu M, Nikhitha KS (2019) Design and implementation of intelligent system to detect malicious facebook posts using support vector machine (SVM). In: Soft computing and medical bioinformatics, Springer, pp 17\u201324","DOI":"10.1007\/978-981-13-0059-2_3"},{"issue":"18","key":"4627_CR32","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1016\/j.fss.2008.01.013","volume":"159","author":"PY Hao","year":"2008","unstructured":"Hao PY et al (2008) Fuzzy one-class support vector machines. Fuzzy Sets Syst 159(18):2317\u20132336","journal-title":"Fuzzy Sets Syst"},{"key":"4627_CR33","doi-asserted-by":"crossref","unstructured":"Heo G, Gader P (2009) Fuzzy svm for noisy data: A robust membership calculation method. In: IEEE international conference on fuzzy systems, 2009. FUZZ-IEEE 2009. IEEE, pp 431\u2013436","DOI":"10.1109\/FUZZY.2009.5277191"},{"key":"4627_CR34","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neucom.2015.12.133","volume":"211","author":"H Huang","year":"2016","unstructured":"Huang H, Wei X, Zhou Y (2016) A sparse method for least squares twin support vector regression. Neurocomputing 211:150\u2013158","journal-title":"Neurocomputing"},{"issue":"1","key":"4627_CR35","first-page":"2185","volume":"15","author":"X Huang","year":"2014","unstructured":"Huang X, Shi L, Suykens JA (2014) Ramp loss linear programming support vector machine. J Mach Learn Res 15(1):2185\u20132211","journal-title":"J Mach Learn Res"},{"issue":"5","key":"4627_CR36","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1109\/TPAMI.2013.178","volume":"36","author":"X Huang","year":"2014","unstructured":"Huang X, Shi L, Suykens JA (2014) Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell 36(5):984\u2013997","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4627_CR37","doi-asserted-by":"crossref","unstructured":"Jeragh M, AlSulaimi M (2018) Combining auto encoders and one class support vectors machine for fraudulant credit card transactions detection. In: 2018 Second world conference on smart trends in systems, security and sustainability (WorldS4), IEEE, pp 178\u2013184","DOI":"10.1109\/WorldS4.2018.8611624"},{"issue":"4","key":"4627_CR38","first-page":"443","volume":"3","author":"J Jiang","year":"2006","unstructured":"Jiang J, Wu C, Song C et al (2006) Adaptive and iterative gene selection based on least squares support vector regression. J Inf Comput Sci 3(4):443\u2013451","journal-title":"J Inf Comput Sci"},{"key":"4627_CR39","first-page":"2017","volume":"3","author":"EM Jordaan","year":"2004","unstructured":"Jordaan EM, Smits GF (2004) Robust outlier detection using SVM regression. IEEE Int Jt Conf Neural Netw 3:2017\u20132022","journal-title":"IEEE Int Jt Conf Neural Netw"},{"key":"4627_CR40","unstructured":"Joshi A, Krishnapuram R (1998) Robust fuzzy clustering methods to support web mining. In: Proceedings of workshop in data mining and knowledge discovery, SIGMOD, Citeseer, pp 1\u201315"},{"key":"4627_CR41","doi-asserted-by":"crossref","unstructured":"Kaya D (2019) Optimization of SVM parameters with hybrid CS-PSO algorithms for Parkinson\u2019s disease in labview environment. Parkinson\u2019s Disease 2019","DOI":"10.1155\/2019\/2513053"},{"key":"4627_CR42","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.asoc.2016.05.025","volume":"47","author":"R Khemchandani","year":"2016","unstructured":"Khemchandani R, Sharma S (2016) Robust least squares twin support vector machine for human activity recognition. Appl Soft Comput 47:33\u201346","journal-title":"Appl Soft Comput"},{"issue":"5","key":"4627_CR43","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"R Khemchandani","year":"2007","unstructured":"Khemchandani R, Chandra S et al (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":"13","key":"4627_CR44","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1016\/j.patrec.2008.05.016","volume":"29","author":"MA Kumar","year":"2008","unstructured":"Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recognit Lett 29(13):1842\u20131848","journal-title":"Pattern Recognit Lett"},{"issue":"4","key":"4627_CR45","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"},{"issue":"9","key":"4627_CR46","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/TPAMI.2008.114","volume":"30","author":"N Kwak","year":"2008","unstructured":"Kwak N (2008) Principal component analysis based on l1-norm maximization. IEEE Trans Pattern Anal Mach Intell 30(9):1672\u20131680","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4627_CR47","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neucom.2014.11.051","volume":"153","author":"HM Le","year":"2015","unstructured":"Le HM, Le Thi HA, Nguyen MC (2015) Sparse semi-supervised support vector machines by DC programming and DCA. Neurocomputing 153:62\u201376","journal-title":"Neurocomputing"},{"issue":"3","key":"4627_CR48","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1023\/A:1008288411710","volume":"11","author":"Hoai A Le Thi","year":"1997","unstructured":"Le Thi Hoai A, Tao PD (1997) Solving a class of linearly constrained indefinite quadratic problems by dc algorithms. J Glob Optim 11(3):253\u2013285","journal-title":"J Glob Optim"},{"issue":"2","key":"4627_CR49","first-page":"76","volume":"8","author":"G Lee","year":"2006","unstructured":"Lee G, Taur J, Tao C (2006) A robust fuzzy support vector machine for two-class pattern classification. Int J Fuzzy Syst 8(2):76\u201386","journal-title":"Int J Fuzzy Syst"},{"issue":"1","key":"4627_CR50","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/02331934.2014.994627","volume":"65","author":"CN Li","year":"2016","unstructured":"Li CN, Shao YH, Deng NY (2016) Robust l1-norm non-parallel proximal support vector machine. Optimization 65(1):169\u2013183","journal-title":"Optimization"},{"key":"4627_CR51","doi-asserted-by":"crossref","unstructured":"Li Q, Li X, Ba W (2015) Sparse least squares support vector machine with l 0-norm in primal space. In: 2015 IEEE international conference on information and automation, IEEE, pp 2778\u20132783","DOI":"10.1109\/ICInfA.2015.7279758"},{"key":"4627_CR52","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.knosys.2018.04.017","volume":"152","author":"Y Li","year":"2018","unstructured":"Li Y, Wang Y, Bi C, Jiang X (2018) Revisiting transductive support vector machines with margin distribution embedding. Knowl Based Syst 152:200\u2013214","journal-title":"Knowl Based Syst"},{"issue":"2","key":"4627_CR53","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/72.991432","volume":"13","author":"CF Lin","year":"2002","unstructured":"Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464\u2013471","journal-title":"IEEE Trans Neural Netw"},{"key":"4627_CR54","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/10984697_11","volume":"177","author":"Cf Lin","year":"2005","unstructured":"Lin Cf, Wang Sd (2005) Fuzzy support vector machines with automatic membership setting. Theory Appl Support Vector Mach 177:233\u2013254","journal-title":"Theory Appl Support Vector Mach"},{"issue":"14","key":"4627_CR55","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1016\/j.patrec.2004.06.009","volume":"25","author":"CF Lin","year":"2004","unstructured":"Lin CF et al (2004) Training algorithms for fuzzy support vector machines with noisy data. Pattern Recognit Lett 25(14):1647\u20131656","journal-title":"Pattern Recognit Lett"},{"key":"4627_CR56","doi-asserted-by":"crossref","unstructured":"Lin CT, Liang SF, Yeh CM, Fan KW (2005) Fuzzy neural network design using support vector regression for function approximation with outliers. In: 2005 IEEE international conference on systems, man and cybernetics, IEEE, vol\u00a03, pp 2763\u20132768","DOI":"10.1109\/ICSMC.2005.1571568"},{"key":"4627_CR57","doi-asserted-by":"crossref","unstructured":"Liu CY, Sun L, Zhou ZJ (2013) Weighted support vector machine based on association rules. In: 2013 International conference on machine learning and cybernetics (ICMLC), IEEE, vol\u00a01, pp 381\u2013386","DOI":"10.1109\/ICMLC.2013.6890498"},{"key":"4627_CR58","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jocs.2016.02.001","volume":"14","author":"D Liu","year":"2016","unstructured":"Liu D, Shi Y, Tian Y, Huang X (2016) Ramp loss least squares support vector machine. J Comput Sci 14:61\u201368","journal-title":"J Comput Sci"},{"issue":"3","key":"4627_CR59","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TPAMI.2015.2456899","volume":"38","author":"T Liu","year":"2016","unstructured":"Liu T, Tao D (2016) Classification with noisy labels by importance reweighting. IEEE Trans Pattern Anal Mach Intell 38(3):447\u2013461","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"4627_CR60","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, Pr\u00edncipe 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":"1","key":"4627_CR61","doi-asserted-by":"crossref","first-page":"11208","DOI":"10.3182\/20110828-6-IT-1002.03467","volume":"44","author":"Y Ma","year":"2011","unstructured":"Ma Y, Li L, Huang X, Wang S (2011) Robust support vector machine using least median loss penalty. IFAC Proc Vol 44(1):11208\u201311213","journal-title":"IFAC Proc Vol"},{"key":"4627_CR62","unstructured":"Mangasarian OL, Wild EW (2001) Proximal support vector machine classifiers. In: Proceedings KDD-2001: knowledge discovery and data mining, Citeseer"},{"issue":"1","key":"4627_CR63","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TPAMI.2006.17","volume":"28","author":"OL Mangasarian","year":"2006","unstructured":"Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69\u201374","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4627_CR64","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.neucom.2014.05.063","volume":"143","author":"S Mehrkanoon","year":"2014","unstructured":"Mehrkanoon S, Huang X, Suykens JA (2014) Non-parallel support vector classifiers with different loss functions. Neurocomputing 143:294\u2013301","journal-title":"Neurocomputing"},{"key":"4627_CR65","doi-asserted-by":"crossref","unstructured":"Mohdiwale S, Sahu TP, Chaurasia RK, Nagwani NK, Verma S (2018) Detection and classification of noise using bark domain features. In: Proceedings of the 6th international conference on communications and broadband networking, ACM, pp 18\u201321","DOI":"10.1145\/3193092.3193098"},{"issue":"3","key":"4627_CR66","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.neuroimage.2011.06.042","volume":"58","author":"J Mour\u00e3o-Miranda","year":"2011","unstructured":"Mour\u00e3o-Miranda J, Hardoon DR, Hahn T, Marquand AF, Williams SC, Shawe-Taylor J, Brammer M (2011) Patient classification as an outlier detection problem: an application of the one-class support vector machine. Neuroimage 58(3):793\u2013804","journal-title":"Neuroimage"},{"key":"4627_CR67","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.sigpro.2014.04.010","volume":"104","author":"JA Nasiri","year":"2014","unstructured":"Nasiri JA, Charkari NM, Mozafari K (2014) Energy-based model of least squares twin support vector machines for human action recognition. Signal Process 104:248\u2013257","journal-title":"Signal Process"},{"key":"4627_CR68","doi-asserted-by":"crossref","unstructured":"Ning K, Liu M, Dong M, Wu Z (2014) Robust ls-svr based on variational bayesian and its applications. In: 2014 International joint conference on neural networks (IJCNN), IEEE, pp 2920\u20132926","DOI":"10.1109\/IJCNN.2014.6889435"},{"issue":"6","key":"4627_CR69","first-page":"4247","volume":"32","author":"J Niu","year":"2017","unstructured":"Niu J, Chen J, Xu Y (2017) Twin support vector regression with huber loss. J Intell Fuzzy Syst 32(6):4247\u20134258","journal-title":"J Intell Fuzzy Syst"},{"key":"4627_CR70","doi-asserted-by":"crossref","unstructured":"Oliva JT, Rosa JLG (2017) The use of one-class classifiers for differentiating healthy from epileptic EEQ segments. In: 2017 International joint conference on neural networks (IJCNN), IEEE, pp 2956\u20132963","DOI":"10.1109\/IJCNN.2017.7966222"},{"issue":"2","key":"4627_CR71","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LSP.2018.2889273","volume":"26","author":"P Oza","year":"2019","unstructured":"Oza P, Patel VM (2019) One-class convolutional neural network. IEEE Signal Process Lett 26(2):277\u2013281","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"4627_CR72","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1002\/cjs.10105","volume":"39","author":"SY Park","year":"2011","unstructured":"Park SY, Liu Y (2011) Robust penalized logistic regression with truncated loss functions. Can J Stat 39(2):300\u2013323","journal-title":"Can J Stat"},{"issue":"20","key":"4627_CR73","doi-asserted-by":"crossref","first-page":"3863","DOI":"10.1016\/j.ins.2010.06.039","volume":"180","author":"X Peng","year":"2010","unstructured":"Peng X (2010) A $$\\nu$$-twin support vector machine ($$\\nu$$-tsvm) classifier and its geometric algorithms. Inf Sci 180(20):3863\u20133875","journal-title":"Inf Sci"},{"issue":"16\u201318","key":"4627_CR74","doi-asserted-by":"crossref","first-page":"2846","DOI":"10.1016\/j.neucom.2010.08.013","volume":"73","author":"X Peng","year":"2010","unstructured":"Peng X (2010) Primal twin support vector regression and its sparse approximation. Neurocomputing 73(16\u201318):2846\u20132858","journal-title":"Neurocomputing"},{"issue":"3","key":"4627_CR75","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.neunet.2009.07.002","volume":"23","author":"X Peng","year":"2010","unstructured":"Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365\u2013372","journal-title":"Neural Netw"},{"issue":"10\u201311","key":"4627_CR76","doi-asserted-by":"crossref","first-page":"2678","DOI":"10.1016\/j.patcog.2011.03.031","volume":"44","author":"X Peng","year":"2011","unstructured":"Peng X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognit 44(10\u201311):2678\u20132692","journal-title":"Pattern Recognit"},{"key":"4627_CR77","doi-asserted-by":"crossref","unstructured":"Rajalaxmi R, Vidhya E (2019) A mutated salp swarm algorithm for optimization of support vector machine parameters. In: 2019 5th International conference on advanced computing & communication systems (ICACCS), IEEE, pp 979\u2013983","DOI":"10.1109\/ICACCS.2019.8728416"},{"key":"4627_CR78","unstructured":"Rakhe SS, Vaidya AS (2015) A survey on different unsupervised techniques to detect outliers. Int Res J Eng Technol (IRJET) 2"},{"key":"4627_CR79","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.sigpro.2016.04.007","volume":"128","author":"A Roy","year":"2016","unstructured":"Roy A, Singha J, Devi SS, Laskar RH (2016) Impulse noise removal using svm classification based fuzzy filter from grayscale images. Signal Process 128:262\u2013273","journal-title":"Signal Process"},{"key":"4627_CR80","unstructured":"Ruff L, G\u00f6rnitz N, Deecke L, Siddiqui SA, Vandermeulen R, Binder A, M\u00fcller E, Kloft M (2018) Deep one-class classification. In: International conference on machine learning, pp 4390\u20134399"},{"key":"4627_CR81","doi-asserted-by":"crossref","unstructured":"Rustam Z, Pandelaki J, Siahaan A, et\u00a0al. (2019) Kernel spherical k-means and support vector machine for acute sinusitis classification. In: IOP conference series: materials science and engineering, IOP Publishing, vol 546, p 052011","DOI":"10.1088\/1757-899X\/546\/5\/052011"},{"issue":"3","key":"4627_CR82","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s11760-013-0479-5","volume":"9","author":"J Saeedi","year":"2015","unstructured":"Saeedi J, Ahadi SM, Faez K (2015) Robust voice activity detection directed by noise classification. Signal Image Video Process 9(3):561\u2013572","journal-title":"Signal Image Video Process"},{"issue":"3","key":"4627_CR83","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/LSP.2012.2216874","volume":"20","author":"YH Shao","year":"2013","unstructured":"Shao YH, Deng NY, Chen WJ, Wang Z (2013) Improved generalized eigenvalue proximal support vector machine. IEEE Signal Process Lett 20(3):213\u2013216","journal-title":"IEEE Signal Process Lett"},{"issue":"1","key":"4627_CR84","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, Jing L, Deng NY (2013) An $$\\varepsilon$$-twin support vector machine for regression. Neural Comput Appl 23(1):175\u2013185","journal-title":"Neural Comput Appl"},{"key":"4627_CR85","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.patcog.2017.03.011","volume":"68","author":"X Shen","year":"2017","unstructured":"Shen X, Niu L, Qi Z, Tian Y (2017) Support vector machine classifier with truncated pinball loss. Pattern Recognit 68:199\u2013210","journal-title":"Pattern Recognit"},{"issue":"3","key":"4627_CR86","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1007\/s10115-010-0323-y","volume":"28","author":"Y Shi","year":"2011","unstructured":"Shi Y, Zhang L (2011) Coid: a cluster-outlier iterative detection approach to multi-dimensional data analysis. Knowl Inf Syst 28(3):709\u2013733","journal-title":"Knowl Inf Syst"},{"issue":"2","key":"4627_CR87","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.cie.2005.01.009","volume":"48","author":"HJ Shin","year":"2005","unstructured":"Shin HJ, Eom DH, Kim SS (2005) One-class support vector machines-an application in machine fault detection and classification. Comput Ind Eng 48(2):395\u2013408","journal-title":"Comput Ind Eng"},{"issue":"3","key":"4627_CR88","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Sch\u00f6lkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199\u2013222","journal-title":"Stat Comput"},{"issue":"4","key":"4627_CR89","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1109\/TSMCC.2002.807277","volume":"32","author":"Q Song","year":"2002","unstructured":"Song Q, Hu W, Xie W (2002) Robust support vector machine with bullet hole image classification. IEEE Trans Syst Man Cybern Part C (Appl Rev) 32(4):440\u2013448","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"key":"4627_CR90","doi-asserted-by":"crossref","first-page":"20334","DOI":"10.1109\/ACCESS.2018.2822546","volume":"6","author":"XQ Sun","year":"2018","unstructured":"Sun XQ, Chen YJ, Shao YH, Li CN, Wang CH (2018) Robust nonparallel proximal support vector machine with lp-norm regularization. IEEE Access 6:20334\u201320347","journal-title":"IEEE Access"},{"issue":"3","key":"4627_CR91","doi-asserted-by":"crossref","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"},{"issue":"1\u20134","key":"4627_CR92","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0925-2312(01)00644-0","volume":"48","author":"JA Suykens","year":"2002","unstructured":"Suykens JA, De Brabanter J, Lukas L, Vandewalle J (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1\u20134):85\u2013105","journal-title":"Neurocomputing"},{"key":"4627_CR93","first-page":"9","volume":"2088\u20138708","author":"M Takruri","year":"2019","unstructured":"Takruri M, Mahmoud A, Khaled M, Al-Jumaily A (2019) PSO-SVM hybrid system for melanoma detection from histo-pathological images. Int J Electr Comput Eng 2088\u20138708:9","journal-title":"Int J Electr Comput Eng"},{"key":"4627_CR94","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.knosys.2018.02.016","volume":"147","author":"L Tang","year":"2018","unstructured":"Tang L, Tian Y, Yang C, Pardalos PM (2018) Ramp-loss nonparallel support vector regression: robust, sparse and scalable approximation. Knowl Based Syst 147:55\u201367","journal-title":"Knowl Based Syst"},{"issue":"1\u20134","key":"4627_CR95","first-page":"23","volume":"133","author":"PD Tao","year":"2005","unstructured":"Tao PD et al (2005) The dc (difference of convex functions) programming and dca revisited with dc models of real world nonconvex optimization problems. Ann Oper Res 133(1\u20134):23\u201346","journal-title":"Ann Oper Res"},{"issue":"3","key":"4627_CR96","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s11063-004-1640-5","volume":"20","author":"Q Tao","year":"2004","unstructured":"Tao Q, Wang J (2004) A new fuzzy support vector machine based on the weighted margin. Neural Process Lett 20(3):139\u2013150","journal-title":"Neural Process Lett"},{"key":"4627_CR97","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.asoc.2018.11.001","volume":"75","author":"Z Tao","year":"2019","unstructured":"Tao Z, Huiling L, Wenwen W, Xia Y (2019) Ga-svm based feature selection and parameter optimization in hospitalization expense modeling. Appl Soft Comput 75:323\u2013332","journal-title":"Appl Soft Comput"},{"issue":"1","key":"4627_CR98","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DM Tax","year":"2004","unstructured":"Tax DM, Duin RP (2004) Support vector data description. Mach Learn 54(1):45\u201366","journal-title":"Mach Learn"},{"key":"4627_CR99","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.neucom.2018.05.027","volume":"310","author":"Y Tian","year":"2018","unstructured":"Tian Y, Mirzabagheri M, Bamakan SMH, Wang H, Qu Q (2018) Ramp loss one-class support vector machine; a robust and effective approach to anomaly detection problems. Neurocomputing 310:223\u2013235","journal-title":"Neurocomputing"},{"issue":"2","key":"4627_CR100","first-page":"69","volume":"6","author":"D Tomar","year":"2014","unstructured":"Tomar D, Agarwal S (2014) Feature selection based least square twin support vector machine for diagnosis of heart disease. Int J Bio-Sci Bio-Technol 6(2):69\u201382","journal-title":"Int J Bio-Sci Bio-Technol"},{"key":"4627_CR101","doi-asserted-by":"crossref","unstructured":"Tuba E, Strumberger I, Bacanin N, Jovanovic R, Tuba M (2019) Bare bones fireworks algorithm for feature selection and SVM optimization. In: 2019 IEEE congress on evolutionary computation (CEC), IEEE, pp 2207\u20132214","DOI":"10.1109\/CEC.2019.8790033"},{"key":"4627_CR102","unstructured":"Vapnik VN (1998) Adaptive and learning systems for signal processing communications, and control. Stat Learn Theory"},{"key":"4627_CR103","doi-asserted-by":"crossref","unstructured":"Vijayalakshmi V, Babu MS, Lakshmi RP (2018) Kfcm algorithm for effective brain stroke detection through SVM classifier. In: 2018 IEEE international conference on system, computation, automation and networking (ICSCA), IEEE, pp 1\u20136","DOI":"10.1109\/ICSCAN.2018.8541179"},{"issue":"1","key":"4627_CR104","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/TSMCB.2007.908862","volume":"38","author":"JS Wang","year":"2008","unstructured":"Wang JS, Chiang JC (2008) A cluster validity measure with outlier detection for support vector clustering. IEEE Trans Syst Man Cybern Part B (Cybern) 38(1):78\u201389","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"key":"4627_CR105","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.knosys.2014.08.003","volume":"71","author":"K Wang","year":"2014","unstructured":"Wang K, Zhong P (2014) Robust non-convex least squares loss function for regression with outliers. Knowl Based Syst 71:290\u2013302","journal-title":"Knowl Based Syst"},{"issue":"13\u201315","key":"4627_CR106","doi-asserted-by":"crossref","first-page":"3020","DOI":"10.1016\/j.neucom.2007.12.032","volume":"71","author":"L Wang","year":"2008","unstructured":"Wang L, Jia H, Li J (2008) Training robust support vector machine with smooth ramp loss in the primal space. Neurocomputing 71(13\u201315):3020\u20133025","journal-title":"Neurocomputing"},{"key":"4627_CR107","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.image.2018.08.003","volume":"71","author":"R Wang","year":"2019","unstructured":"Wang R, Li W, Li R, Zhang L (2019) Automatic blur type classification via ensemble svm. Signal Process Image Commun 71:24\u201335","journal-title":"Signal Process Image Commun"},{"issue":"4","key":"4627_CR108","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.ipm.2006.09.011","volume":"43","author":"T-Y Wang","year":"2007","unstructured":"Wang T-Y, Chiang H-M (2007) Fuzzy support vector machine for multi-class text categorization. Inf Process Manag 43(4):914\u2013929","journal-title":"Inf Process Manag"},{"issue":"6","key":"4627_CR109","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/TFUZZ.2005.859320","volume":"13","author":"Y Wang","year":"2005","unstructured":"Wang Y, Wang S, Lai KK (2005) A new fuzzy support vector machine to evaluate credit risk. IEEE Trans Fuzzy Syst 13(6):820\u2013831","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"4627_CR110","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.scs.2018.11.031","volume":"45","author":"YF Wang","year":"2019","unstructured":"Wang YF, Jiong Y, Su GP, Qian YR (2019) A new outlier detection method based on optics. Sustain Cities Soc 45:197\u2013212","journal-title":"Sustain Cities Soc"},{"issue":"10","key":"4627_CR111","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/TNNLS.2014.2379930","volume":"26","author":"Z Wang","year":"2015","unstructured":"Wang Z, Shao YH, Bai L, Deng NY (2015) Twin support vector machine for clustering. IEEE Trans Neural Netw Learn Syst 26(10):2583\u20132588","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4627_CR112","unstructured":"Wang Z, Chen X, Li CN, Shao YH (2018) Ramp-based twin support vector clustering. arXiv preprint arXiv:181203710"},{"issue":"9","key":"4627_CR113","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.3390\/app9091761","volume":"9","author":"Z Wang","year":"2019","unstructured":"Wang Z, Wang S, Kong D, Liu S (2019) Methane detection based on improved chicken algorithm optimization support vector machine. Appl Sci 9(9):1761","journal-title":"Appl Sci"},{"issue":"12","key":"4627_CR114","doi-asserted-by":"crossref","first-page":"7788","DOI":"10.1016\/j.eswa.2010.04.061","volume":"37","author":"Q Wu","year":"2010","unstructured":"Wu Q, Law R (2010) Fuzzy support vector regression machine with penalizing gaussian noises on triangular fuzzy number space. Expert Syst Appl 37(12):7788\u20137795","journal-title":"Expert Syst Appl"},{"issue":"479","key":"4627_CR115","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1198\/016214507000000617","volume":"102","author":"Y Wu","year":"2007","unstructured":"Wu Y, Liu Y (2007) Robust truncated hinge loss support vector machines. J Am Stat Assoc 102(479):974\u2013983","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"4627_CR116","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1080\/10618600.2012.680866","volume":"22","author":"Y Wu","year":"2013","unstructured":"Wu Y, Liu Y (2013) Adaptively weighted large margin classifiers. J Comput Graph Stat 22(2):416\u2013432","journal-title":"J Comput Graph Stat"},{"key":"4627_CR117","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.physa.2018.10.060","volume":"517","author":"J Xiao","year":"2019","unstructured":"Xiao J (2019) SVM and KNN ensemble learning for traffic incident detection. Phys A Stat Mech Appl 517:29\u201335","journal-title":"Phys A Stat Mech Appl"},{"key":"4627_CR118","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.chemolab.2015.11.010","volume":"151","author":"Y Xiao","year":"2016","unstructured":"Xiao Y, Wang H, Xu W, Zhou J (2016) Robust one-class SVM for fault detection. Chemom Intell Lab Syst 151:15\u201325","journal-title":"Chemom Intell Lab Syst"},{"key":"4627_CR119","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.patrec.2016.11.016","volume":"85","author":"Y Xiao","year":"2017","unstructured":"Xiao Y, Wang H, Xu W (2017) Ramp loss based robust one-class SVM. Pattern Recognit Lett 85:15\u201320","journal-title":"Pattern Recognit Lett"},{"key":"4627_CR120","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.patcog.2018.07.015","volume":"84","author":"HJ Xing","year":"2018","unstructured":"Xing HJ, Ji M (2018) Robust one-class support vector machine with rescaled hinge loss function. Pattern Recognit 84:152\u2013164","journal-title":"Pattern Recognit"},{"key":"4627_CR121","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.patcog.2016.09.045","volume":"63","author":"G Xu","year":"2017","unstructured":"Xu G, Cao Z, Hu BG, Principe JC (2017) Robust support vector machines based on the rescaled hinge loss function. Pattern Recognit 63:139\u2013148","journal-title":"Pattern Recognit"},{"key":"4627_CR122","unstructured":"Xu H, Caramanis C, Mannor S (2009) Robust regression and lasso. In: Advances in neural information processing systems, pp 1801\u20131808"},{"key":"4627_CR123","first-page":"536","volume":"6","author":"L Xu","year":"2006","unstructured":"Xu L, Crammer K, Schuurmans D (2006) Robust support vector machine training via convex outlier ablation. AAAI 6:536\u2013542","journal-title":"AAAI"},{"issue":"1","key":"4627_CR124","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s10489-013-0500-2","volume":"41","author":"Y Xu","year":"2014","unstructured":"Xu Y, Guo R (2014) An improved $$\\nu$$-twin support vector machine. Appl Intell 41(1):42\u201354","journal-title":"Appl Intell"},{"key":"4627_CR125","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.knosys.2012.03.013","volume":"33","author":"Y Xu","year":"2012","unstructured":"Xu Y, Wang L (2012) A weighted twin support vector regression. Knowl Based Syst 33:92\u2013101","journal-title":"Knowl Based Syst"},{"issue":"6","key":"4627_CR126","first-page":"1773","volume":"30","author":"Y Xu","year":"2014","unstructured":"Xu Y, Lv X, Wang Z, Wang L (2014) A weighted least squares twin support vector machine. J Inf Sci Eng 30(6):1773\u20131787","journal-title":"J Inf Sci Eng"},{"issue":"2","key":"4627_CR127","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TNNLS.2015.2513006","volume":"28","author":"Y Xu","year":"2017","unstructured":"Xu Y, Yang Z, Pan X (2017) 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":"4627_CR128","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neunet.2014.06.007","volume":"57","author":"HY Yang","year":"2014","unstructured":"Yang HY, Wang XY, Niu PP, Liu YC (2014) Image denoising using nonsubsampled shearlet transform and twin support vector machines. Neural Netw 57:152\u2013165","journal-title":"Neural Netw"},{"key":"4627_CR129","doi-asserted-by":"crossref","unstructured":"Yang J, Deng T, Sui R (2016) An adaptive weighted one-class svm for robust outlier detection. In: Proceedings of the 2015 Chinese intelligent systems conference, Springer, pp 475\u2013484","DOI":"10.1007\/978-3-662-48386-2_49"},{"key":"4627_CR130","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.chemolab.2018.04.003","volume":"177","author":"L Yang","year":"2018","unstructured":"Yang L, Dong H (2018) Support vector machine with truncated pinball loss and its application in pattern recognition. Chemom Intell Lab Syst 177:89\u201399","journal-title":"Chemom Intell Lab Syst"},{"key":"4627_CR131","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.engappai.2016.04.003","volume":"53","author":"L Yang","year":"2016","unstructured":"Yang L, Zhang S (2016) A sparse extreme learning machine framework by continuous optimization algorithms and its application in pattern recognition. Eng Appl Artif Intell 53:176\u2013189","journal-title":"Eng Appl Artif Intell"},{"issue":"11","key":"4627_CR132","doi-asserted-by":"crossref","first-page":"3014","DOI":"10.1162\/neco_a_01002","volume":"29","author":"L Yang","year":"2017","unstructured":"Yang L, Ren Z, Wang Y, Dong H (2017) A robust regression framework with laplace kernel-induced loss. Neural Comput 29(11):3014\u20133039","journal-title":"Neural Comput"},{"issue":"05","key":"4627_CR133","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1142\/S0218001407005703","volume":"21","author":"X Yang","year":"2007","unstructured":"Yang X, Song Q, Wang Y (2007) A weighted support vector machine for data classification. Int J Pattern Recognit Artif Intell 21(05):961\u2013976","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"4627_CR134","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neucom.2014.03.037","volume":"140","author":"X Yang","year":"2014","unstructured":"Yang X, Tan L, He L (2014) A robust least squares support vector machine for regression and classification with noise. Neurocomputing 140:41\u201352","journal-title":"Neurocomputing"},{"issue":"10","key":"4627_CR135","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1007\/s00500-014-1448-9","volume":"19","author":"X Yang","year":"2015","unstructured":"Yang X, Han L, Li Y, He L (2015) A bilateral-truncated-loss based robust support vector machine for classification problems. Soft Comput 19(10):2871\u20132882","journal-title":"Soft Comput"},{"issue":"1","key":"4627_CR136","first-page":"105","volume":"19","author":"W YangX","year":"2011","unstructured":"YangX W, ZhangG Q et al (2011) A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises. IEEE Trans FuzzySyst 19(1):105\u2013115","journal-title":"IEEE Trans FuzzySyst"},{"issue":"9","key":"4627_CR137","doi-asserted-by":"crossref","first-page":"4494","DOI":"10.1109\/TNNLS.2017.2749428","volume":"29","author":"Q Ye","year":"2018","unstructured":"Ye Q, Zhao H, Li Z, Yang X, Gao S, Yin T, Ye N (2018) L1-norm distance minimization-based fast robust twin support vector $$k$$-plane clustering. IEEE Trans Neural Netw Learn Syst 29(9):4494\u20134503","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4627_CR138","first-page":"32","volume":"305","author":"YF Ye","year":"2017","unstructured":"Ye YF, Shao YH, Deng NY, Li CN, Hua XY (2017) Robust lp-norm least squares support vector regression with feature selection. Appl Math Comput 305:32\u201352","journal-title":"Appl Math Comput"},{"key":"4627_CR139","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.neucom.2014.05.035","volume":"145","author":"S Yin","year":"2014","unstructured":"Yin S, Zhu X, Jing C (2014) Fault detection based on a robust one class support vector machine. Neurocomputing 145:263\u2013268","journal-title":"Neurocomputing"},{"key":"4627_CR140","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1016\/j.proeng.2011.08.251","volume":"15","author":"L You","year":"2011","unstructured":"You L, Jizhen L, Yaxin Q (2011) A new robust least squares support vector machine for regression with outliers. Procedia Eng 15:1355\u20131360","journal-title":"Procedia Eng"},{"issue":"4","key":"4627_CR141","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1162\/08997660360581958","volume":"15","author":"AL Yuille","year":"2003","unstructured":"Yuille AL, Rangarajan A (2003) The concave\u2013convex procedure. Neural Comput 15(4):915\u2013936","journal-title":"Neural Comput"},{"issue":"2","key":"4627_CR142","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10844-009-0102-y","volume":"35","author":"Y Zhang","year":"2010","unstructured":"Zhang Y, Xie F, Huang D, Ji M (2010) Support vector classifier based on fuzzy c-means and mahalanobis distance. J Intell Inf Syst 35(2):333\u2013345","journal-title":"J Intell Inf Syst"},{"issue":"10","key":"4627_CR143","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.neunet.2008.09.001","volume":"21","author":"Y Zhao","year":"2008","unstructured":"Zhao Y, Sun J (2008) Robust support vector regression in the primal. Neural Netw 21(10):1548\u20131555","journal-title":"Neural Netw"},{"key":"4627_CR144","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"},{"issue":"2","key":"4627_CR145","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s00521-011-0525-6","volume":"21","author":"P Zhong","year":"2012","unstructured":"Zhong P, Xu Y, Zhao Y (2012) Training twin support vector regression via linear programming. Neural Comput Appl 21(2):399\u2013407","journal-title":"Neural Comput Appl"},{"key":"4627_CR146","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2015.10.097","volume":"189","author":"F Zhu","year":"2016","unstructured":"Zhu F, Yang J, Gao C, Xu S, Ye N, Yin T (2016) A weighted one-class support vector machine. Neurocomputing 189:1\u201310","journal-title":"Neurocomputing"},{"issue":"1","key":"4627_CR147","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s00357-018-9249-y","volume":"35","author":"W Zhu","year":"2018","unstructured":"Zhu W, Song Y, Xiao Y (2018) A new support vector machine plus with pinball loss. J Classif 35(1):52\u201370","journal-title":"J Classif"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04627-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-019-04627-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04627-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T08:23:13Z","timestamp":1749802993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-019-04627-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,2]]},"references-count":147,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["4627"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04627-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,2]]},"assertion":[{"value":"7 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}