{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:32:42Z","timestamp":1761611562523},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:00:00Z","timestamp":1699228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:00:00Z","timestamp":1699228800000},"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":["J Supercomput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11227-023-05711-4","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T13:03:02Z","timestamp":1699275782000},"page":"7207-7244","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Distributed independent vector machine for big data classification problems"],"prefix":"10.1007","volume":"80","author":[{"given":"Mohammad Hassan","family":"Almaspoor","sequence":"first","affiliation":[]},{"given":"Ali A.","family":"Safaei","sequence":"additional","affiliation":[]},{"given":"Afshin","family":"Salajegheh","sequence":"additional","affiliation":[]},{"given":"Behrouz","family":"Minaei-Bidgoli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"5711_CR1","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.patcog.2019.04.012","volume":"95","author":"TI Dhamecha","year":"2019","unstructured":"Dhamecha TI, Noore A, Singh R, Vatsa M (2019) Between-subclass piece-wise linear solutions in large scale kernel SVM learning. Pattern Recognit 95:173\u2013190. https:\/\/doi.org\/10.1016\/j.patcog.2019.04.012","journal-title":"Pattern Recognit"},{"issue":"1","key":"5711_CR2","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s40537-015-0030-3","volume":"2","author":"CW Tsai","year":"2015","unstructured":"Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: a survey. J Big Data 2(1):21. https:\/\/doi.org\/10.1186\/s40537-015-0030-3","journal-title":"J Big Data"},{"key":"5711_CR3","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2014.10.102","volume":"172","author":"XJ Shen","year":"2016","unstructured":"Shen XJ, Mu L, Li Z, Wu HX, Gou JP, Chen X (2016) Large-scale support vector machine classification with redundant data reduction. Neurocomputing 172:189\u2013197. https:\/\/doi.org\/10.1016\/j.neucom.2014.10.102","journal-title":"Neurocomputing"},{"key":"5711_CR4","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.neucom.2020.05.054","volume":"407","author":"S Peng","year":"2020","unstructured":"Peng S, Hu Q, Dang J, Wang W (2020) Optimal feasible step-size based working set selection for large scale SVMs training. Neurocomputing 407:366\u2013375. https:\/\/doi.org\/10.1016\/j.neucom.2020.05.054","journal-title":"Neurocomputing"},{"issue":"2","key":"5711_CR5","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/LSP.2004.836938","volume":"12","author":"BY Sun","year":"2005","unstructured":"Sun BY, Huang DS, Fang HT (2005) Lidar signal denoising using least-squares support vector machine. IEEE Signal Process Lett 12(2):101\u2013104. https:\/\/doi.org\/10.1109\/LSP.2004.836938","journal-title":"IEEE Signal Process Lett"},{"issue":"2","key":"5711_CR6","doi-asserted-by":"publisher","first-page":"185","DOI":"10.2174\/092986607779816078","volume":"14","author":"P Chen","year":"2007","unstructured":"Chen P, Wang B, Wong HS, Huang DS (2007) Prediction of protein B-factors using multi-class bounded SVM. Protein Peptide Lett 14(2):185\u2013190. https:\/\/doi.org\/10.2174\/092986607779816078","journal-title":"Protein Peptide Lett"},{"key":"5711_CR7","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/J.NEUCOM.2017.03.060","volume":"247","author":"X Liang","year":"2017","unstructured":"Liang X, Zhu L, Huang DS (2017) Multi-task ranking SVM for image cosegmentation. Neurocomputing 247:126\u2013136. https:\/\/doi.org\/10.1016\/J.NEUCOM.2017.03.060","journal-title":"Neurocomputing"},{"key":"5711_CR8","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/J.ASOC.2015.08.048","volume":"37","author":"J Cervantes","year":"2015","unstructured":"Cervantes J, Garc\u00eda Lamont F, L\u00f3pez-Chau A, Rodr\u00edguez Mazahua L, Sergio Ru\u00edz J (2015) Data selection based on decision tree for SVM classification on large data sets. Appl Soft Comput 37:787\u2013798. https:\/\/doi.org\/10.1016\/J.ASOC.2015.08.048","journal-title":"Appl Soft Comput"},{"key":"5711_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT.2017.8203926","author":"VA Naik","year":"2017","unstructured":"Naik VA, Desai AA (2017) Online handwritten Gujarati character recognition using SVM, MLP, and K-NN, 8th Int. Conf Comput Commun Netw Technol ICCCNT. https:\/\/doi.org\/10.1109\/ICCCNT.2017.8203926","journal-title":"Conf Comput Commun Netw Technol ICCCNT"},{"key":"5711_CR10","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/S10032-009-0084-X","volume":"12","author":"TK Bhowmik","year":"2009","unstructured":"Bhowmik TK, Ghanty P, Roy A, Parui SK (2009) SVM-based hierarchical architectures for handwritten Bangla character recognition. Int J Doc Anal Recognit IJDAR 12:97\u2013108. https:\/\/doi.org\/10.1007\/S10032-009-0084-X","journal-title":"Int J Doc Anal Recognit IJDAR"},{"key":"5711_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.10.118","author":"J Cervantes","year":"2020","unstructured":"Cervantes J, Garcia-Lamont F, Rodr\u00edguez-Mazahua L, Lopez A (2020) A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2019.10.118","journal-title":"Neurocomputing"},{"key":"5711_CR12","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.5555\/945365.964289","volume":"4","author":"I Gov","year":"2003","unstructured":"Gov I (2003) Sparseness of support vector machines Ingo Steinwart. J Mach Learn Res 4:1071\u20131105. https:\/\/doi.org\/10.5555\/945365.964289","journal-title":"J Mach Learn Res"},{"issue":"5","key":"5711_CR13","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1007\/s00521-011-0793-1","volume":"22","author":"J Zheng","year":"2013","unstructured":"Zheng J, Shen F, Fan H, Zhao J (2013) An online incremental learning support vector machine for large-scale data. Neural Comput Appl 22(5):1023\u20131035. https:\/\/doi.org\/10.1007\/s00521-011-0793-1","journal-title":"Neural Comput Appl"},{"key":"5711_CR14","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.procs.2018.10.517","volume":"144","author":"RFW Pratama","year":"2018","unstructured":"Pratama RFW, Purnami SW, Rahayu SP (2018) Boosting support vector machines for imbalanced microarray data. Proced Comput Sci 144:174\u2013183. https:\/\/doi.org\/10.1016\/j.procs.2018.10.517","journal-title":"Proced Comput Sci"},{"key":"5711_CR15","doi-asserted-by":"publisher","unstructured":"Lee YJ and Mangasarian OL (2001) RSVM: Reduced support vector machines, pp 1\u201317, https:\/\/doi.org\/10.1137\/1.9781611972719.13","DOI":"10.1137\/1.9781611972719.13"},{"key":"5711_CR16","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","volume":"237","author":"L Zhou","year":"2017","unstructured":"Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: opportunities and challenges. Neurocomputing 237:350\u2013361. https:\/\/doi.org\/10.1016\/j.neucom.2017.01.026","journal-title":"Neurocomputing"},{"issue":"2","key":"5711_CR17","doi-asserted-by":"publisher","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 Discov 2(2):121\u2013167. https:\/\/doi.org\/10.1023\/A:1009715923555","journal-title":"Data Min Knowl Discov"},{"key":"5711_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1","author":"VN Vapnik","year":"2000","unstructured":"Vapnik VN (2000) The nature of statistical learning theory. Springer New York. https:\/\/doi.org\/10.1007\/978-1-4757-3264-1","journal-title":"Springer New York"},{"issue":"6","key":"5711_CR19","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1162\/1532443041827925","volume":"4","author":"I Steinwart","year":"2004","unstructured":"Steinwart I (2004) Sparseness of support vector machines. J Mach Learn Res 4(6):1071\u20131105. https:\/\/doi.org\/10.1162\/1532443041827925","journal-title":"J Mach Learn Res"},{"key":"5711_CR20","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1109\/GrC.2010.46","volume":"2010","author":"X Li","year":"2010","unstructured":"Li X, Cervantes J, Yu W (2010) A novel SVM classification method for large data sets, Proc\u20142010. IEEE Int Conf Granul Comput GrC 2010:297\u2013302. https:\/\/doi.org\/10.1109\/GrC.2010.46","journal-title":"IEEE Int Conf Granul Comput GrC"},{"issue":"2","key":"5711_CR21","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"CW Hsu","year":"2002","unstructured":"Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415\u2013425. https:\/\/doi.org\/10.1109\/72.991427","journal-title":"IEEE Trans Neural Netw"},{"issue":"4","key":"5711_CR22","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1016\/j.patcog.2009.09.021","volume":"43","author":"F Orabona","year":"2010","unstructured":"Orabona F, Castellini C, Caputo B, Jie L, Sandini G (2010) On-line independent support vector machines. Pattern Recognit 43(4):1402\u20131412. https:\/\/doi.org\/10.1016\/j.patcog.2009.09.021","journal-title":"Pattern Recognit"},{"key":"5711_CR23","doi-asserted-by":"publisher","first-page":"7164","DOI":"10.1109\/ACCESS.2017.2779794","volume":"6","author":"A Rojas-Dominguez","year":"2017","unstructured":"Rojas-Dominguez A, Padierna LC, Carpio Valadez JM, Puga-Soberanes HJ, Fraire HJ (2017) Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access 6:7164\u20137176. https:\/\/doi.org\/10.1109\/ACCESS.2017.2779794","journal-title":"IEEE Access"},{"issue":"9","key":"5711_CR24","doi-asserted-by":"publisher","first-page":"5560","DOI":"10.1109\/TPAMI.2021.3075339","volume":"44","author":"S Zhou","year":"2022","unstructured":"Zhou S (2022) Sparse SVM for sufficient data reduction. IEEE Trans Pattern Anal Mach Intell 44(9):5560\u20135571. https:\/\/doi.org\/10.1109\/TPAMI.2021.3075339","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"5711_CR25","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/TPAMI.2005.77","volume":"27","author":"JX Dong","year":"2005","unstructured":"Dong JX, Krzyzak A, Suen CY (2005) Fast SVM training algorithm with decomposition on very large data sets. IEEE Trans Pattern Anal Mach Intell 27(4):603\u2013618. https:\/\/doi.org\/10.1109\/TPAMI.2005.77","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5711_CR26","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1145\/1150402.1150429","volume":"2006","author":"T Joachims","year":"2006","unstructured":"Joachims T (2006) Training linear SVMs in linear time, Proc. ACM SIGKDD. Int Conf Knowl Discov Data Min 2006:217\u2013226. https:\/\/doi.org\/10.1145\/1150402.1150429","journal-title":"Int Conf Knowl Discov Data Min"},{"key":"5711_CR27","unstructured":"Graf HP, Cosatto E, Bottou L, Durdanovic I and Vapnik V (2005) Parallel support vector machines\u202f: the cascade SVM, Adv Neural Inf Process Syst, pp 521\u2013528"},{"key":"5711_CR28","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/RIVF.2006.1696420","volume":"760","author":"TN Do","year":"2006","unstructured":"Do TN, Poulet F (2006) Classifying one billion data with a new distributed SVM algorithm. RIVF. 760:59\u201366. https:\/\/doi.org\/10.1109\/RIVF.2006.1696420","journal-title":"RIVF."},{"issue":"3","key":"5711_CR29","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JAK Suykens","year":"1999","unstructured":"Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293\u2013300. https:\/\/doi.org\/10.1023\/A:1018628609742","journal-title":"Neural Process Lett"},{"issue":"4","key":"5711_CR30","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1109\/TNN.2006.875968","volume":"17","author":"A Navia-V\u00e1zquez","year":"2006","unstructured":"Navia-V\u00e1zquez A, Guti\u00e9rrez-Gonz\u00e1lez D, Parrado-Hern\u00e1ndez E, Navarro-Abell\u00e1n JJ (2006) Distributed support vector machines. IEEE Trans Neural Netw 17(4):1091\u20131097. https:\/\/doi.org\/10.1109\/TNN.2006.875968","journal-title":"IEEE Trans Neural Netw"},{"issue":"7","key":"5711_CR31","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1109\/TNN.2007.2000061","volume":"19","author":"Y Lu","year":"2008","unstructured":"Lu Y, Roychowdhury V, Vandenberghe L (2008) Distributed parallel support vector machines in strongly connected networks. IEEE Trans Neural Netw 19(7):1167\u20131178. https:\/\/doi.org\/10.1109\/TNN.2007.2000061","journal-title":"IEEE Trans Neural Netw"},{"key":"5711_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-20429-6_10","volume":"2","author":"EY Chang","year":"2009","unstructured":"Chang EY et al (2009) PSVM Parallelizing support vector machines on distributed computers. Adv Neural Inf Process Syst Proc Conf 2:1\u20138. https:\/\/doi.org\/10.1007\/978-3-642-20429-6_10","journal-title":"Adv Neural Inf Process Syst Proc Conf"},{"issue":"7","key":"5711_CR33","doi-asserted-by":"publisher","first-page":"2801","DOI":"10.1016\/j.camwa.2011.07.046","volume":"62","author":"NK Alham","year":"2011","unstructured":"Alham NK, Li M, Liu Y, Hammoud S (2011) A MapReduce-based distributed SVM algorithm for automatic image annotation. Comput Math with Appl 62(7):2801\u20132811. https:\/\/doi.org\/10.1016\/j.camwa.2011.07.046","journal-title":"Comput Math with Appl"},{"issue":"1","key":"5711_CR34","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s11063-015-9472-z","volume":"44","author":"W Guo","year":"2016","unstructured":"Guo W, Alham NK, Liu Y, Li M, Qi M (2016) A resource aware Mapreduce based parallel SVM for large scale image classifications. Neural Process Lett 44(1):161\u2013184. https:\/\/doi.org\/10.1007\/s11063-015-9472-z","journal-title":"Neural Process Lett"},{"key":"5711_CR35","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2014.05.072","volume":"145","author":"ZH You","year":"2014","unstructured":"You ZH, Yu JZ, Zhu L, Li S, Wen ZK (2014) A MapReduce based parallel SVM for large-scale predicting protein-protein interactions. Neurocomputing 145:37\u201343. https:\/\/doi.org\/10.1016\/j.neucom.2014.05.072","journal-title":"Neurocomputing"},{"key":"5711_CR36","doi-asserted-by":"publisher","unstructured":"Do TN and Poulet F (2017) Parallel learning of local SVM algorithms for classifying large datasets, in Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 10140 LNCS, pp 67\u201393. https:\/\/doi.org\/10.1007\/978-3-662-54173-9_4.","DOI":"10.1007\/978-3-662-54173-9_4"},{"key":"5711_CR37","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neunet.2016.04.007","volume":"80","author":"S Scardapane","year":"2016","unstructured":"Scardapane S, Fierimonte R, Di Lorenzo P, Panella M, Uncini A (2016) Distributed semi-supervised support vector machines. Neural Netw 80:43\u201352","journal-title":"Neural Netw"},{"key":"5711_CR38","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.ins.2018.07.045","volume":"466","author":"Y Liu","year":"2018","unstructured":"Liu Y, Xu Z, Li C (2018) Distributed online semi-supervised support vector machine. Inf Sci (Ny) 466:236\u2013257. https:\/\/doi.org\/10.1016\/j.ins.2018.07.045","journal-title":"Inf Sci (Ny)"},{"key":"5711_CR39","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1109\/LCSYS.2021.3086388","volume":"6","author":"M Doostmohammadian","year":"2022","unstructured":"Doostmohammadian M, Aghasi A, Charalambous T, Khan UA (2022) Distributed support vector machines over dynamic balanced directed networks. IEEE Control Syst Lett 6:758\u2013763. https:\/\/doi.org\/10.1109\/LCSYS.2021.3086388","journal-title":"IEEE Control Syst Lett"},{"key":"5711_CR40","doi-asserted-by":"publisher","first-page":"114154","DOI":"10.1016\/J.ESWA.2020.114154","volume":"167","author":"R Kashef","year":"2021","unstructured":"Kashef R (2021) A boosted SVM classifier trained by incremental learning and decremental unlearning approach. Expert Syst Appl 167:114154. https:\/\/doi.org\/10.1016\/J.ESWA.2020.114154","journal-title":"Expert Syst Appl"},{"key":"5711_CR41","doi-asserted-by":"publisher","unstructured":"Laskar S and Adnan MA (2022) Fast support vector machine using singular value decomposition, Proc 2022 IEEE International Conference on Big Data, Big Data 2022, pp 1280\u20131285, https:\/\/doi.org\/10.1109\/BIGDATA55660.2022.10020978","DOI":"10.1109\/BIGDATA55660.2022.10020978"},{"key":"5711_CR42","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-981-16-0708-0_6\/COVER","volume":"1374","author":"D Patel","year":"2021","unstructured":"Patel D (2021) Quantile regression support vector machine (QRSVM) model for time series data analysis. Commun Comput Inf Sci 1374:65\u201374. https:\/\/doi.org\/10.1007\/978-981-16-0708-0_6\/COVER","journal-title":"Commun Comput Inf Sci"},{"issue":"4","key":"5711_CR43","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/S0167-8191(03)00021-8","volume":"29","author":"G Zanghirati","year":"2003","unstructured":"Zanghirati G, Zanni L (2003) A parallel solver for large quadratic programs in training support vector machines. Parallel Comput 29(4):535\u2013551. https:\/\/doi.org\/10.1016\/S0167-8191(03)00021-8","journal-title":"Parallel Comput"},{"key":"5711_CR44","first-page":"121","volume":"61","author":"T Eitrich","year":"2006","unstructured":"Eitrich T, Lang B (2006) On the optimal working set size in serial and parallel support vector machine learning with the decomposition algorithm. Conf Res Pract Inf Technol Ser 61:121\u2013128","journal-title":"Conf Res Pract Inf Technol Ser"},{"key":"5711_CR45","doi-asserted-by":"publisher","unstructured":"Serafini T, Zanni L and Zanghirati G (2005) Some improvements to a parallel decomposition technique for training support vector machines, in: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 3666 LNCS, pp 9\u201317. https:\/\/doi.org\/10.1007\/11557265_7","DOI":"10.1007\/11557265_7"},{"key":"5711_CR46","doi-asserted-by":"publisher","unstructured":"Qiu S and Lane T (2005) Parallel computation of RBF kernels for support vector classifiers, In: Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005, pp 334\u2013345, https:\/\/doi.org\/10.1137\/1.9781611972757.30","DOI":"10.1137\/1.9781611972757.30"},{"issue":"6","key":"5711_CR47","doi-asserted-by":"publisher","first-page":"897","DOI":"10.3233\/IDA-2012-00558","volume":"16","author":"X Li","year":"2012","unstructured":"Li X, Cervantes J, Yu W (2012) Fast classification for large data sets via random selection clustering and support vector machines. Intell Data Anal 16(6):897\u2013914. https:\/\/doi.org\/10.3233\/IDA-2012-00558","journal-title":"Intell Data Anal"},{"issue":"1","key":"5711_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNN.2006.883722","volume":"18","author":"YJ Lee","year":"2007","unstructured":"Lee YJ, Huang SY (2007) Reduced support vector machines: a statistical theory. IEEE Trans Neural Netw 18(1):1\u201313. https:\/\/doi.org\/10.1109\/TNN.2006.883722","journal-title":"IEEE Trans Neural Netw"},{"key":"5711_CR49","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.asoc.2013.12.009","volume":"16","author":"F Zhu","year":"2014","unstructured":"Zhu F, Yang J, Ye N, Gao C, Li G, Yin T (2014) Neighbors\u2019 distribution property and sample reduction for support vector machines. Appl Soft Comput J 16:201\u2013209. https:\/\/doi.org\/10.1016\/j.asoc.2013.12.009","journal-title":"Appl Soft Comput J"},{"issue":"4","key":"5711_CR50","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s00454-001-0006-2","volume":"25","author":"B G\u00e4rtner","year":"2001","unstructured":"G\u00e4rtner B, Welzl E (2001) A simple sampling lemma: analysis and applications in geometric optimization. Discret Comput Geom 25(4):569\u2013590. https:\/\/doi.org\/10.1007\/s00454-001-0006-2","journal-title":"Discret Comput Geom"},{"key":"5711_CR51","doi-asserted-by":"crossref","unstructured":"Loosli G, Canu S and Bottou L (2007) Training invariant support vector machines using selective sampling, Large Scale Kernel Mach, pp 301\u2013320","DOI":"10.7551\/mitpress\/7496.003.0015"},{"issue":"4","key":"5711_CR52","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1007\/s00224-007-9094-6","volume":"42","author":"JL Balc\u00e1zar","year":"2008","unstructured":"Balc\u00e1zar JL, Dai Y, Tanaka J, Watanabe O (2008) Provably fast training algorithms for support vector machines. Theory Comput Syst 42(4):568\u2013595. https:\/\/doi.org\/10.1007\/s00224-007-9094-6","journal-title":"Theory Comput Syst"},{"key":"5711_CR53","doi-asserted-by":"publisher","unstructured":"Chang CC and Lee YJ (2004) Generating the reduced set by systematic sampling, Lect Notes Comput Sci Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics, vol 3177, pp 720\u2013725, https:\/\/doi.org\/10.1007\/978-3-540-28651-6_107","DOI":"10.1007\/978-3-540-28651-6_107"},{"issue":"1","key":"5711_CR54","doi-asserted-by":"publisher","first-page":"183","DOI":"10.6688\/JISE.2010.26.1.13","volume":"26","author":"LIJ Chien","year":"2010","unstructured":"Chien LIJ, Chang CC, Lee YJ (2010) Variant methods of reduced set selection for reduced support vector machines. J Inf Sci Eng 26(1):183\u2013196. https:\/\/doi.org\/10.6688\/JISE.2010.26.1.13","journal-title":"J Inf Sci Eng"},{"key":"5711_CR55","unstructured":"Zain JM (2020) An alternative algorithm for classification large categorical dataset: k-mode clustering reduced support vector machine, Sersc Org, Accessed: 16"},{"key":"5711_CR56","doi-asserted-by":"publisher","unstructured":"Yin C, Zhu Y, Mu S and Tian S (2012) Local support vector machine based on cooperative clustering for very large-scale dataset, International Conference on Natural Computation, Icnc, pp 88\u201392, https:\/\/doi.org\/10.1109\/ICNC.2012.6234598","DOI":"10.1109\/ICNC.2012.6234598"},{"key":"5711_CR57","doi-asserted-by":"publisher","unstructured":"Romero E, Barrio I and Belanche L (2007) Incremental and decremental learning for linear support vector machines, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 4668 LNCS, no PART 1, pp 209\u2013218, https:\/\/doi.org\/10.1007\/978-3-540-74690-4_22","DOI":"10.1007\/978-3-540-74690-4_22"},{"key":"5711_CR58","doi-asserted-by":"publisher","unstructured":"Sch\u00f6lkopf B, Herbrich R and Smola AJ (2001) A generalized representer theorem, pp 416\u2013426, https:\/\/doi.org\/10.1007\/3-540-44581-1_27","DOI":"10.1007\/3-540-44581-1_27"},{"issue":"8","key":"5711_CR59","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1109\/TSP.2004.830985","volume":"52","author":"Y Engel","year":"2004","unstructured":"Engel Y, Mannor S, Meir R (2004) The kernel recursive least-squares algorithm. IEEE Trans Signal Process 52(8):2275\u20132285. https:\/\/doi.org\/10.1109\/TSP.2004.830985","journal-title":"IEEE Trans Signal Process"},{"key":"5711_CR60","unstructured":"Platt JC (2021) Sequential minimal optimization: a fast algorithm for training support vector machines. Apr. 21, 1998. Accessed: Dec. 02"},{"issue":"3","key":"5711_CR61","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1162\/089976601300014493","volume":"13","author":"SS Keerthi","year":"2001","unstructured":"Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to Platt\u2019s SMO algorithm for SVM classifier design. Neural Comput 13(3):637\u2013649. https:\/\/doi.org\/10.1162\/089976601300014493","journal-title":"Neural Comput"},{"issue":"4","key":"5711_CR62","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s00256-009-0861-0","volume":"39","author":"EE Drakonaki","year":"2010","unstructured":"Drakonaki EE, Allen GM (2010) Spark: cluster computing withworking sets matei. Skeletal Radiol 39(4):391\u2013396. https:\/\/doi.org\/10.1007\/s00256-009-0861-0","journal-title":"Skeletal Radiol"},{"issue":"4","key":"5711_CR63","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1109\/TNN.2006.875989","volume":"17","author":"LJ Cao","year":"2006","unstructured":"Cao LJ et al (2006) Parallel sequential minimal optimization for the training of support vector machines. IEEE Trans Neural Netw 17(4):1039\u20131049. https:\/\/doi.org\/10.1109\/TNN.2006.875989","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"5711_CR64","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1002\/WICS.164","volume":"3","author":"NJ Higham","year":"2011","unstructured":"Higham NJ (2011) Gaussian elimination. Wiley Interdiscip Rev Comput Stat 3(3):230\u2013238. https:\/\/doi.org\/10.1002\/WICS.164","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"issue":"2","key":"5711_CR65","doi-asserted-by":"publisher","first-page":"130","DOI":"10.2307\/2322413","volume":"94","author":"SC Althoen","year":"1987","unstructured":"Althoen SC, McLaughlin R (1987) Gauss-Jordan reduction: a brief history. Am Math Mon 94(2):130. https:\/\/doi.org\/10.2307\/2322413","journal-title":"Am Math Mon"},{"issue":"3","key":"5711_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol TIST 2(3):1\u201327. https:\/\/doi.org\/10.1145\/1961189.1961199","journal-title":"ACM Trans Intell Syst Technol TIST"},{"issue":"5","key":"5711_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0155119","volume":"11","author":"T Razzaghi","year":"2016","unstructured":"Razzaghi T, Roderick O, Safro I, Marko N (2016) Multilevel weighted support vector machine for classification on healthcare data with missing values. PLoS ONE 11(5):1\u201318. https:\/\/doi.org\/10.1371\/journal.pone.0155119","journal-title":"PLoS ONE"},{"key":"5711_CR68","first-page":"94104","volume":"340","author":"J Han","year":"2006","unstructured":"Han J, Kamber M, Mining D (2006) Concepts and techniques. Morgan Kaufmann 340:94104\u2013103205","journal-title":"Morgan Kaufmann"},{"issue":"8","key":"5711_CR69","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/J.PATREC.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861\u2013874. https:\/\/doi.org\/10.1016\/J.PATREC.2005.10.010","journal-title":"Pattern Recognit Lett"},{"key":"5711_CR70","doi-asserted-by":"publisher","unstructured":"A study of cross-validation and bootstrap for accuracy estimation and model selection | Proceedings of the 14th international joint conference on Artificial intelligence, Vol 2 https:\/\/doi.org\/10.5555\/1643031.1643047","DOI":"10.5555\/1643031.1643047"},{"key":"5711_CR71","doi-asserted-by":"publisher","unstructured":"Orriols-Puig A, Sastry K, Goldberg DE and Bernad\u00f3-Mansilla E (2006) Substructural surrogates for learning decomposable classification problems, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 4998 LNAI, pp 235\u2013254, https:\/\/doi.org\/10.1007\/978-3-540-88138-4_14","DOI":"10.1007\/978-3-540-88138-4_14"},{"issue":"200","key":"5711_CR72","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701. https:\/\/doi.org\/10.1080\/01621459.1937.10503522","journal-title":"J Am Stat Assoc"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05711-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05711-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05711-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T11:35:41Z","timestamp":1711366541000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05711-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,6]]},"references-count":72,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5711"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05711-4","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,6]]},"assertion":[{"value":"6 October 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}