{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:06:58Z","timestamp":1773965218547,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2013,2,27]],"date-time":"2013-02-27T00:00:00Z","timestamp":1361923200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2014,8]]},"DOI":"10.1007\/s13042-013-0153-9","type":"journal-article","created":{"date-parts":[[2013,2,25]],"date-time":"2013-02-25T22:31:57Z","timestamp":1361831517000},"page":"579-597","source":"Crossref","is-referenced-by-count":11,"title":["A nonlinear least squares quasi-Newton strategy for LP-SVR hyper-parameters selection"],"prefix":"10.1007","volume":"5","author":[{"given":"Pablo","family":"Rivas-Perea","sequence":"first","affiliation":[]},{"given":"Juan","family":"Cota-Ruiz","sequence":"additional","affiliation":[]},{"given":"Jose-Gerardo","family":"Rosiles","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,2,27]]},"reference":[{"key":"153_CR1","doi-asserted-by":"crossref","unstructured":"Anguita D, Boni A, Ridella S, Rivieccio F, Sterpi D (2005) Theoretical and practical model selection methods for support vector classifiers. In: Support vector machines: theory and applications, Springer, Berlin, pp 159\u2013179","DOI":"10.1007\/10984697_7"},{"issue":"1\u20132","key":"153_CR2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0925-2312(03)00430-2","volume":"55","author":"D. Anguita","year":"2003","unstructured":"Anguita D, Ridella S, Rivieccio F, Zunino R (2003) Hyperparameter design criteria for support vector classifiers. Neurocomputing 55(1\u20132):109\u2013134","journal-title":"Neurocomputing"},{"key":"153_CR3","doi-asserted-by":"crossref","unstructured":"Arg\u00e1ez M, Vel\u00e1zquez L (2003) A new infeasible interior-point algorithm for linear programming. In: Proceedings of the 2003 conference on diversity in computing, TAPIA \u201903, ACM, New York, pp 12\u201314. doi: 10.1145\/948542.948545","DOI":"10.1145\/948542.948545"},{"issue":"1","key":"153_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2140\/pjm.1966.16.1","volume":"16","author":"L. Armijo","year":"1966","unstructured":"Armijo L (1966) Minimization of functions having lipschitz continuous first partial derivatives. Pac J Math 16(1):1\u20133","journal-title":"Pac J Math"},{"issue":"3","key":"153_CR5","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0168-1699(99)00046-0","volume":"24","author":"J. Blackard","year":"1999","unstructured":"Blackard J, Dean D (1999) Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Comput Electr Agric 24(3):131\u2013151","journal-title":"Comput Electr Agric"},{"key":"153_CR6","doi-asserted-by":"crossref","unstructured":"Cawley G (2006) Leave-one-out cross-validation based model selection criteria for weighted ls-svms. In: Proceedings of the IEEE international joint conference on neural networks, IJCNN\u201906, pp 1661\u20131668. doi: 10.1109\/IJCNN.2006.246634","DOI":"10.1109\/IJCNN.2006.246634"},{"issue":"5","key":"153_CR7","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1162\/0899766053491869","volume":"17","author":"M. Chang","year":"2005","unstructured":"Chang M, Lin C (2005) Leave-one-out bounds for support vector regression model selection. Neural Comput 17(5):1188\u20131222","journal-title":"Neural Comput"},{"issue":"1","key":"153_CR8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0893-6080(03)00169-2","volume":"17","author":"V. Cherkassky","year":"2004","unstructured":"Cherkassky V, Ma Y (2004) Practical selection of svm parameters and noise estimation for svm regression. Neural Netw 17(1):113\u2013126","journal-title":"Neural Netw"},{"key":"153_CR9","first-page":"143","volume":"1","author":"R. Collobert","year":"2001","unstructured":"Collobert R, Bengio S (2001) Svmtorch: support vector machines for large-scale regression problems. J Mach Learn Res 1:143\u2013160. doi: 10.1162\/15324430152733142","journal-title":"J Mach Learn Res"},{"key":"153_CR10","unstructured":"Courant R, Hilbert D (1966) Methods of mathematical physics. Interscience, New York"},{"key":"153_CR11","doi-asserted-by":"crossref","unstructured":"Dennis J, Schnabel R (1996) Numerical methods for unconstrained optimization and nonlinear equations. Society for Industrial Mathematics, Philadelphia","DOI":"10.1137\/1.9781611971200"},{"key":"153_CR12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0925-2312(02)00601-X","volume":"51","author":"K. Duan","year":"2003","unstructured":"Duan K, Keerthi S, Poo A (2003) Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51:41\u201359","journal-title":"Neurocomputing"},{"key":"153_CR13","first-page":"1","volume":"31","author":"T. Fawcett","year":"2004","unstructured":"Fawcett T (2004) Roc graphs: notes and practical considerations for researchers. Mach Learn 31:1\u201338","journal-title":"Mach Learn"},{"issue":"2","key":"153_CR14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"R. Fisher","year":"1936","unstructured":"Fisher R (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179\u2013188","journal-title":"Ann Eugen"},{"key":"153_CR15","unstructured":"Forina M, Leardi R, Armanino C, Lanteri S (1998) PARVUS: an extendable package of programs for data exploration, classification and correlation. Institute of Pharmaceutical and Food Analysis Technologies, Genoa, Italy"},{"key":"153_CR16","unstructured":"Frank A, Asuncion A (2010) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"153_CR17","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0893-6080(88)90023-8","volume":"1","author":"R. Gorman","year":"1988","unstructured":"Gorman R, Sejnowski T (1988) Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netw 1(1):75\u201389","journal-title":"Neural Netw"},{"key":"153_CR18","unstructured":"Hart P, Duda R, Stork D (2001) Pattern classification. Wiley, New York"},{"key":"153_CR19","unstructured":"Haykin SS (2009) Neural networks and learning machines. Prentice Hall, Upper Saddle River"},{"issue":"1","key":"153_CR20","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s13042-011-0013-4","volume":"2","author":"Q. He","year":"2011","unstructured":"He Q, Wu C (2011) Separating theorem of samples in banach space for support vector machine learning. Int J Mach Learn Cybern 2(1):49\u201354","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"153_CR21","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1145\/360569.360658","volume":"18","author":"M. Hestenes","year":"1975","unstructured":"Hestenes M (1975) Pseudoinversus and conjugate gradients. Commun ACM 18(1):40\u201343","journal-title":"Commun ACM"},{"key":"153_CR22","first-page":"58","volume":"12","author":"Z. Hui-ren","year":"2008","unstructured":"Hui-ren Z, Pi-e Z (2008) Method for selecting parameters of least squares support vector machines based on GA and bootstrap. J Syst Simul 12:58. doi: http:\/\/en.cnki.com.cn\/Article_en\/CJFDTOTAL-XTFZ200607074.htm","journal-title":"J Syst Simul"},{"key":"153_CR23","doi-asserted-by":"crossref","unstructured":"Ito K, Nakano R (2003) Optimizing support vector regression hyperparameters based on cross-validation. In: Proceedings of the IEEE international Joint Conference on neural networks, vol 3, pp 2077\u20132082","DOI":"10.1109\/IJCNN.2003.1223728"},{"key":"153_CR24","doi-asserted-by":"crossref","unstructured":"Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Machine learning ECML-98, Computer Science Department, University of Dortmund,\u00a0pp 137\u2013142","DOI":"10.1007\/BFb0026683"},{"key":"153_CR25","unstructured":"Joachims T (1999) Making large-scale support vector machine learning practical. In: Advances in kernel methods, MIT Press, Cambridge, pp 169\u2013184"},{"key":"153_CR26","unstructured":"Karasuyama M, Kitakoshi D, Nakano R (2006) Revised optimizer of svr hyperparameters minimizing cross-validation error. In: Proceedings of the IEEE international joint conference on neural networks, IJCNN\u201906, pp 319\u2013326"},{"key":"153_CR27","doi-asserted-by":"crossref","unstructured":"Karasuyama M, Nakano R (2007) Optimizing svr hyperparameters via fast cross-validation using aosvr. In: Proceedings of the IEEE international joint conference on neural networks, IJCNN 2007, pp 1186\u20131191","DOI":"10.1109\/IJCNN.2007.4371126"},{"issue":"5","key":"153_CR28","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.ijepes.2009.11.001","volume":"32","author":"A. Karsaz","year":"2010","unstructured":"Karsaz A, Mashhadi H, Mirsalehi M (2010) Market clearing price and load forecasting using cooperative co-evolutionary approach. Int J Electr Power Energy Syst 32(5):408\u2013415","journal-title":"Int J Electr Power Energy Syst"},{"key":"153_CR29","doi-asserted-by":"crossref","unstructured":"Kay S (2006) Intuitive probability and random processes using MATLAB, 1st edn. Springer, Berlin. doi: 10.1007\/b104645","DOI":"10.1007\/b104645"},{"key":"153_CR30","doi-asserted-by":"crossref","unstructured":"Khemchandani R, Karpatne A, Chandra S (2012) Twin support vector regression for the simultaneous learning of a function and its derivatives. Int J Mach Learn Cybern, Springer, pp 1\u201313. doi: 10.1007\/s13042-012-0072-1","DOI":"10.1007\/s13042-012-0072-1"},{"key":"153_CR31","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/978-3-540-89694-4_50","volume":"5361","author":"D. Kinzett","year":"2008","unstructured":"Kinzett D, Zhang M, Johnston M (2008) Using numerical simplification to control bloat in genetic programming. Simul Evol Learn 5361:493\u2013502. doi: 10.1007\/978-3-540-89694-4_50","journal-title":"Simul Evol Learn"},{"key":"153_CR32","doi-asserted-by":"crossref","unstructured":"Kobayashi K, Kitakoshi D, Nakano R (2005) Yet faster method to optimize svr hyperparameters based on minimizing cross-validation error. In: Proceedings of the 2005 IEEE international joint conference on neural networks, IJCNN\u201905, vol 2, pp 871\u2013876. doi: 10.1109\/IJCNN.2005.1555967","DOI":"10.1109\/IJCNN.2005.1555967"},{"key":"153_CR33","unstructured":"Kohavi R (1996) Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid. In: Proceedings of the second international conference on knowledge discovery and data mining, vol 7. Menlo Park, AAAI Press, USA"},{"key":"153_CR34","unstructured":"Lang K, Witbrock M (1988) Learning to tell two spirals apart. In: Proceedings of the 1988 connectionist models summer school, pp 52\u201359 (M. Kaufmann)"},{"issue":"11","key":"153_CR35","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y. LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. doi: 10.1109\/5.726791","journal-title":"Proc IEEE"},{"issue":"1","key":"153_CR36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s13042-011-0012-5","volume":"2","author":"Z. Liu","year":"2011","unstructured":"Liu Z, Wu Q, Zhang Y, Philip Chen C (2011) Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery. Int J Mach Learn Cybern 2(1):37\u201347. doi: 10.1007\/s13042-011-0012-5","journal-title":"Int J Mach Learn Cybern"},{"issue":"7","key":"153_CR37","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1016\/j.matcom.2008.10.011","volume":"79","author":"Z. Lu","year":"2009","unstructured":"Lu Z, Sun J, Butts KR (2009) Linear programming support vector regression with wavelet kernel: a new approach to nonlinear dynamical systems identification. Math Comput Simul 79(7):2051\u20132063. doi: 10.1016\/j.matcom.2008.10.011","journal-title":"Math Comput Simul"},{"issue":"11","key":"153_CR38","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1162\/089976603322385117","volume":"15","author":"J. Ma","year":"2003","unstructured":"Ma J, Theiler J, Perkins S (2003) Accurate on-line support vector regression. Neural Comput 15(11):2683\u20132703. doi: 10.1162\/089976603322385117","journal-title":"Neural Comput"},{"issue":"3","key":"153_CR39","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1080\/00401706.1973.10489073","volume":"15","author":"G. McDonald","year":"1973","unstructured":"McDonald G, Schwing R (1973) Instabilities of regression estimates relating air pollution to mortality. Technometrics 15(3):463\u2013481. doi: 10.2307\/1266852","journal-title":"Technometrics"},{"key":"153_CR40","doi-asserted-by":"crossref","unstructured":"Mercer J (1909) Functions of positive and negative type, and their connection with the theory of integral equations. Philos Trans R Soc Lond Ser A (containing papers of a mathematical or physical character) 209:415\u2013446. doi: 10.1098\/rsta.1909.0016","DOI":"10.1098\/rsta.1909.0016"},{"key":"153_CR41","doi-asserted-by":"crossref","unstructured":"Momma M, Bennett K (2002) A pattern search method for model selection of support vector regression. In: Proceedings of the SIAM international conference on data mining, SIAM, Philadelphia, pp 261\u2013274","DOI":"10.1137\/1.9781611972726.16"},{"key":"153_CR42","doi-asserted-by":"crossref","unstructured":"Musa A (2012) Comparative study on classification performance between support vector machine and logistic regression. Int J Mach Learn Cybern, 1\u201312. doi: 10.1007\/s13042-012-0068-x","DOI":"10.1007\/s13042-012-0068-x"},{"issue":"3","key":"153_CR43","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1093\/oxfordjournals.aje.a115365","volume":"130","author":"D. Nierenberg","year":"1989","unstructured":"Nierenberg D, Stukel T, Baron J, Dain B, Greenberg E (1989) Determinants of plasma levels of beta-carotene and retinol. Skin cancer prevention study group. Am J Epidemiol 130(3):511\u2013521","journal-title":"Am J Epidemiol"},{"key":"153_CR44","doi-asserted-by":"crossref","unstructured":"Nocedal J, Wright S (1999) Numerical optimization. Springer, Berlin. doi: 10.1007\/b98874","DOI":"10.1007\/b98874"},{"issue":"16","key":"153_CR45","doi-asserted-by":"crossref","first-page":"3683","DOI":"10.1016\/j.neucom.2009.07.009","volume":"72","author":"E. Ortiz-Garc\u00eda","year":"2009","unstructured":"Ortiz-Garc\u00eda E, Salcedo-Sanz S, P\u00e9rez-Bellido \u00c1, Portilla-Figueras J (2009) Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions. Neurocomputing 72(16):3683\u20133691. doi: 10.1016\/j.neucom.2009.07.009","journal-title":"Neurocomputing"},{"key":"153_CR46","doi-asserted-by":"crossref","unstructured":"Osuna E, Castro O (2002) Convex hull in feature space for support vector machines. In: Advances in artificial intelligence IBERAMIA 2002, lecture notes in computer science, vol 2527, Springer, Berlin, pp 411\u2013419. doi: 10.1007\/3-540-36131-6_42","DOI":"10.1007\/3-540-36131-6_42"},{"issue":"3","key":"153_CR47","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. doi: 10.1016\/j.neunet.2009.07.002","journal-title":"Neural Netw"},{"issue":"17","key":"153_CR48","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1249\/00005768-198504000-00037","volume":"2","author":"K. Penrose","year":"1985","unstructured":"Penrose K, Nelson A, Fisher A (1985) Generalized body composition prediction equation for men using simple measurement techniques. Med Sci Sports Exerc 2(17):189","journal-title":"Med Sci Sports Exerc"},{"key":"153_CR49","unstructured":"Platt J (1999) Using analytic qp and sparseness to speed training of support vector machines. In: Proceedings of the 1998 conference on Advances in neural information processing systems II, MIT Press, Cambridge, MA, USA, pp 557\u2013563"},{"key":"153_CR50","doi-asserted-by":"crossref","unstructured":"Quinlan J (1993) Combining instance-based and model-based learning. In: Proceedings of the 10th international conference on machine learning, pp 236\u2013243","DOI":"10.1016\/B978-1-55860-307-3.50037-X"},{"issue":"8","key":"153_CR51","first-page":"1160","volume":"5","author":"Y. Ren","year":"2010","unstructured":"Ren Y, Bai G (2010) Determination of optimal svm parameters by using ga\/pso. J Comput 5(8):1160\u20131168. doi: 10.4304\/jcp.5.8.1160-1168","journal-title":"J Comput"},{"key":"153_CR52","unstructured":"Ripley B (2008) Pattern recognition and neural networks, 1st edn. Cambridge University Press, Cambridge"},{"key":"153_CR53","unstructured":"Rivas-Perea P (2009) Southwestern US and northwestern mexico dust storm modeling trough moderate resolution imaging spectroradiometer data: a machine learning perspective. Technical report: NASA\/UMBC\/GEST graduate student summer program. http:\/\/gest.umbc.edu\/student_opp\/2009_gssp_reports.html"},{"key":"153_CR54","unstructured":"Rivas Perea P (2011) Algorithms for training large-scale linear programming support vector regression and classification. PhD thesis, The University of Texas at El Paso"},{"key":"153_CR55","doi-asserted-by":"crossref","unstructured":"Rivas-Perea P, Cota-Ruiz J (2012) An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods. Pattern Recogn Lett (In Press). doi: 10.1016\/j.patrec.2012.10.026","DOI":"10.1016\/j.patrec.2012.10.026"},{"issue":"5","key":"153_CR56","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B. Sch\u00f6lkopf","year":"2000","unstructured":"Sch\u00f6lkopf B, Smola A, Williamson R, Bartlett P (2000) New support vector algorithms. Neural Comput 12(5):1207\u20131245. doi: 10.1162\/089976600300015565","journal-title":"Neural Comput"},{"issue":"1-4","key":"153_CR57","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s13042-010-0003-y","volume":"1","author":"K. Small","year":"2010","unstructured":"Small K, Roth D (2010) Margin-based active learning for structured predictions. Int J Mach Learn Cybern 1(1\u20134):3\u201325. doi: 10.1007\/s13042-010-0003-y","journal-title":"Int J Mach Learn Cybern"},{"key":"153_CR58","doi-asserted-by":"crossref","unstructured":"Smets K, Verdonk B, Jordaan E (2007) Evaluation of performance measures for svr hyperparameter selection. In: Proceedings of the IEEE international joint conference on neural networks, IJCNN 2007, pp. 637\u2013642. doi: 10.1109\/IJCNN.2007.4371031","DOI":"10.1109\/IJCNN.2007.4371031"},{"issue":"3","key":"153_CR59","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"A.J. Smola","year":"2004","unstructured":"Smola AJ, Sch\u00f6lkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199\u2013222. doi: 10.1023\/B:STCO.0000035301.49549.88","journal-title":"Stat Comput"},{"key":"153_CR60","unstructured":"Stark H, Woods J (2001) Probability and random processes with applications to signal processing, 3rd edn. Prentice-Hall, Upper Saddle River"},{"issue":"4-6","key":"153_CR61","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.neucom.2008.04.008","volume":"72","author":"Y. Torii","year":"2009","unstructured":"Torii Y, Abe S (2009) Decomposition techniques for training linear programming support vector machines. Neurocomputing 72(4-6):973\u2013984. doi: 10.1016\/j.neucom.2008.04.008","journal-title":"Neurocomputing"},{"key":"153_CR62","first-page":"281","volume":"9","author":"V. Vapnik","year":"1997","unstructured":"Vapnik V, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. Adv Neural Inf Process Syst 9:281\u2013287","journal-title":"Adv Neural Inf Process Syst"},{"key":"153_CR63","doi-asserted-by":"crossref","unstructured":"Wang L (2005) Support vector machines: theory and applications, studies in fuzziness and soft computing, vol 177, Springer, Berlin","DOI":"10.1007\/b95439"},{"key":"153_CR64","unstructured":"Waugh S (1995) Extending and benchmarking cascade-correlation. PhD thesis, University of Tasmania, Tasmania"},{"issue":"1","key":"153_CR65","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s13042-011-0035-y","volume":"3","author":"J.Z. Xiao","year":"2012","unstructured":"Xiao JZ, Wang HR, Yang XC, Gao Z (2012) Multiple faults diagnosis in motion system based on svm. Int J Mach Learn Cybern 3(1):77\u201382. doi: 10.1007\/s13042-011-0035-y","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"153_CR66","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S1004-4132(08)60066-3","volume":"19","author":"Y. Xiaofang","year":"2008","unstructured":"Xiaofang Y, Yaonan W (2008) Parameter selection of support vector machine for function approximation based on chaos optimization. J Syst Eng Electr 19(1):191\u2013197. doi: 10.1016\/S1004-4132(08)60066-3","journal-title":"J Syst Eng Electr"},{"issue":"7","key":"153_CR67","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/j.neunet.2008.11.005","volume":"22","author":"Z. Xu","year":"2009","unstructured":"Xu Z, Huang K, Zhu J, King I, Lyu MR (2009) A novel kernel-based maximum a posteriori classification method. Neural Netw 22(7):977\u2013987. doi: 10.1016\/j.neunet.2008.11.005","journal-title":"Neural Netw"},{"issue":"12","key":"153_CR68","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1016\/S0008-8846(98)00165-3","volume":"28","author":"I. Yeh","year":"1998","unstructured":"Yeh I (1998) Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete research 28(12):1797\u20131808. doi: 10.1016\/S0008-8846(98)00165-3","journal-title":"Cement Concr Res"},{"key":"153_CR69","doi-asserted-by":"crossref","unstructured":"Zhang JP, Li ZW, Yang J (2005) A parallel svm training algorithm on large-scale classification problems. In: Proceedings of the 2005 international conference on machine learning and cybernetics, vol 3, pp 1637\u20131641. doi: 10.1109\/icmlc.2005.1527207","DOI":"10.1109\/ICMLC.2005.1527207"},{"issue":"3","key":"153_CR70","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.neunet.2009.11.012","volume":"23","author":"L. Zhang","year":"2010","unstructured":"Zhang L, Zhou W (2010) On the sparseness of 1-norm support vector machines. Neural Netw 23(3):373\u2013385. doi: 10.1016\/j.neunet.2009.11.012","journal-title":"Neural Netw"},{"key":"153_CR71","doi-asserted-by":"crossref","unstructured":"Zhang XQ, Gu CH (2007) Ch-svm based network anomaly detection. In: Proceedings of the 2007 international conference on machine learning and cybernetics, vol 6, pp 3261 \u20133266. doi: 10.1109\/icmlc.2007.4370710","DOI":"10.1109\/ICMLC.2007.4370710"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-013-0153-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-013-0153-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-013-0153-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,9]],"date-time":"2019-07-09T23:33:23Z","timestamp":1562715203000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-013-0153-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,2,27]]},"references-count":71,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,8]]}},"alternative-id":["153"],"URL":"https:\/\/doi.org\/10.1007\/s13042-013-0153-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,2,27]]}}}