{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:56:19Z","timestamp":1774626979379,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"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":["Mach Learn"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10994-022-06238-0","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T18:03:54Z","timestamp":1666202634000},"page":"1567-1594","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Parametric non-parallel support vector machines for pattern classification"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8835-285X","authenticated-orcid":false,"given":"Sambhav","family":"Jain","sequence":"first","affiliation":[]},{"given":"Reshma","family":"Rastogi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"6238_CR1","unstructured":"Asuncion, A., & Newman, D. (2007). Uci machine learning repository."},{"key":"6238_CR2","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.neucom.2019.09.069","volume":"376","author":"WJ Chen","year":"2020","unstructured":"Chen, W. J., Shao, Y. H., Li, C. N., et al. (2020). $$\\nu$$-projection twin support vector machine for pattern classification. Neurocomputing, 376, 10\u201324.","journal-title":"Neurocomputing"},{"issue":"3","key":"6238_CR3","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. Machine learning, 20(3), 273\u2013297.","journal-title":"Machine learning"},{"key":"6238_CR4","doi-asserted-by":"publisher","unstructured":"Diethe, T. (2015). 13 benchmark datasets derived from the UCI, DELVE and STATLOG repositories. https:\/\/github.com\/tdiethe\/gunnar_raetsch_benchmark_datasets\/, https:\/\/doi.org\/10.5281\/zenodo.18110","DOI":"10.5281\/zenodo.18110"},{"issue":"1","key":"6238_CR5","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TDEI.2010.5412017","volume":"17","author":"L Hao","year":"2010","unstructured":"Hao, L., & Lewin, P. (2010). Partial discharge source discrimination using a support vector machine. IEEE Transactions on Dielectrics and electrical Insulation, 17(1), 189\u2013197.","journal-title":"IEEE Transactions on Dielectrics and electrical Insulation"},{"issue":"1","key":"6238_CR6","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.neunet.2009.08.001","volume":"23","author":"PY Hao","year":"2010","unstructured":"Hao, P. Y. (2010). New support vector algorithms with parametric insensitive\/margin model. Neural Networks, 23(1), 60\u201373.","journal-title":"Neural Networks"},{"key":"6238_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.178","author":"X Huang","year":"2014","unstructured":"Huang, X., Shi, L., & Suykens, J. (2014). Support vector machine classifier with pinball loss. IEEE Transactions on Pattern Analysis and Machine Intelligence. https:\/\/doi.org\/10.1109\/TPAMI.2013.178","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"10","key":"6238_CR8","doi-asserted-by":"publisher","first-page":"4723","DOI":"10.1109\/TIT.2009.2027527","volume":"55","author":"N Hurley","year":"2009","unstructured":"Hurley, N., & Rickard, S. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10), 4723\u20134741.","journal-title":"IEEE Transactions on Information Theory"},{"key":"6238_CR9","doi-asserted-by":"crossref","unstructured":"Jayadeva. (2015). Learning a hyperplane classifier by minimizing an exact bound on the vc dimensioni. Neurocomputing, 149, 683\u2013689.","DOI":"10.1016\/j.neucom.2014.07.062"},{"issue":"2","key":"6238_CR10","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TITS.2013.2282635","volume":"15","author":"HG Jung","year":"2013","unstructured":"Jung, H. G., & Kim, G. (2013). Support vector number reduction: Survey and experimental evaluations. IEEE Transactions on Intelligent Transportation Systems, 15(2), 463\u2013476.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"5","key":"6238_CR11","doi-asserted-by":"publisher","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 Transactions on Pattern Analysis and Machine Intelligence, 29(5), 905\u2013910.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"9","key":"6238_CR12","doi-asserted-by":"publisher","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 Transactions on Pattern Analysis and Machine Intelligence, 30(9), 1672\u20131680.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"6238_CR13","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/02331934.2014.994627","volume":"65","author":"CN Li","year":"2016","unstructured":"Li, C. N., Shao, Y. H., & Deng, N. Y. (2016). Robust l1-norm non-parallel proximal support vector machine. Optimization, 65(1), 169\u2013183. https:\/\/doi.org\/10.1080\/02331934.2014.994627","journal-title":"Optimization"},{"issue":"1","key":"6238_CR14","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TPAMI.2006.17","volume":"28","author":"OL Mangasarian","year":"2005","unstructured":"Mangasarian, O. L., & Wild, E. W. (2005). Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1), 69\u201374.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"6238_CR15","volume-title":"Matlab","author":"S Matlab","year":"2012","unstructured":"Matlab, S. (2012). Matlab. Natick, MA: The MathWorks."},{"key":"6238_CR16","unstructured":"Musicant, D.R. (1998). NDC: Normally distributed clustered datasets. Www.cs.wisc.edu\/dmi\/svm\/ndc\/"},{"issue":"2","key":"6238_CR17","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s10462-017-9611-1","volume":"52","author":"J Nalepa","year":"2019","unstructured":"Nalepa, J., & Kawulok, M. (2019). Selecting training sets for support vector machines: A review. Artificial Intelligence Review, 52(2), 857\u2013900.","journal-title":"Artificial Intelligence Review"},{"issue":"10\u201311","key":"6238_CR18","doi-asserted-by":"publisher","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 Recognition, 44(10\u201311), 2678\u20132692.","journal-title":"Pattern Recognition"},{"key":"6238_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.01.023","author":"X Peng","year":"2016","unstructured":"Peng, X., Xu, D., Kong, L., et al. (2016). L1-norm loss based twin support vector machine for data recognition. Information Sciences. https:\/\/doi.org\/10.1016\/j.ins.2016.01.023.","journal-title":"Information Sciences"},{"key":"6238_CR20","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.neucom.2018.08.079","volume":"322","author":"R Rastogi","year":"2018","unstructured":"Rastogi, R., Pal, A., & Chandra, S. (2018). Generalized pinball loss SVMs. Neurocomputing, 322, 151\u2013165.","journal-title":"Neurocomputing"},{"issue":"6","key":"6238_CR21","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1109\/TNN.2011.2130540","volume":"22","author":"YH Shao","year":"2011","unstructured":"Shao, Y. H., Zhang, C. H., Wang, X. B., et al. (2011). Improvements on twin support vector machines. IEEE Transactions on Neural Networks, 22(6), 962\u2013968.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"6238_CR22","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ins.2013.11.003","volume":"263","author":"YH Shao","year":"2014","unstructured":"Shao, Y. H., Chen, W. J., & Deng, N. Y. (2014). Nonparallel hyperplane support vector machine for binary classification problems. Information Sciences, 263, 22\u201335.","journal-title":"Information Sciences"},{"issue":"5","key":"6238_CR23","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.compbiomed.2013.01.020","volume":"43","author":"A Subasi","year":"2013","unstructured":"Subasi, A. (2013). Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Computers in Biology and Medicine, 43(5), 576\u2013586.","journal-title":"Computers in Biology and Medicine"},{"key":"6238_CR24","doi-asserted-by":"crossref","unstructured":"Tanveer, M., Rajani, T., Rastogi, R., et\u00a0al. (2022). Comprehensive review on twin support vector machines. Annals of Operations Research, 1\u201346.","DOI":"10.1007\/s10479-022-04575-w"},{"issue":"7","key":"6238_CR25","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1109\/TCYB.2013.2279167","volume":"44","author":"Y Tian","year":"2013","unstructured":"Tian, Y., Qi, Z., Ju, X., et al. (2013). Nonparallel support vector machines for pattern classification. IEEE Transactions on Cybernetics, 44(7), 1067\u20131079.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"5","key":"6238_CR26","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"VN Vapnik","year":"1999","unstructured":"Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988\u2013999.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"10","key":"6238_CR27","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1109\/TNNLS.2014.2379930","volume":"26","author":"Z Wang","year":"2015","unstructured":"Wang, Z., Shao, Y. H., Bai, L., et al. (2015). Twin support vector machine for clustering. IEEE Transactions on Neural Networks and Learning Systems, 26(10), 2583\u20132588.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"2","key":"6238_CR28","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/TNNLS.2015.2513006","volume":"28","author":"Y Xu","year":"2016","unstructured":"Xu, Y., Yang, Z., & Pan, X. (2016). A novel twin support-vector machine with pinball loss. IEEE Transactions on Neural Networks and Learning Systems, 28(2), 359\u2013370.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"7","key":"6238_CR29","doi-asserted-by":"publisher","first-page":"3368","DOI":"10.1016\/j.eswa.2014.11.069","volume":"42","author":"H Yin","year":"2015","unstructured":"Yin, H., Jiao, X., Chai, Y., et al. (2015). Scene classification based on single-layer SAE and SVM. Expert Systems with Applications, 42(7), 3368\u20133380.","journal-title":"Expert Systems with Applications"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06238-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06238-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06238-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T17:08:40Z","timestamp":1711645720000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06238-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,19]]},"references-count":29,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["6238"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06238-0","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,19]]},"assertion":[{"value":"30 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no conflicts of interest in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research paper does not involve any studies with human participants or animals performed by any authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}