{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:30:22Z","timestamp":1772141422978,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T00:00:00Z","timestamp":1546473600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51475418"],"award-info":[{"award-number":["51475418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51490663"],"award-info":[{"award-number":["51490663"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51475417"],"award-info":[{"award-number":["51475417"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s00521-018-03981-1","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T02:41:39Z","timestamp":1546483299000},"page":"4849-4864","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A novel support vector regression algorithm incorporated with prior knowledge and error compensation for small datasets"],"prefix":"10.1007","volume":"31","author":[{"given":"Zhenyu","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yunkun","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Chan","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Jianrong","family":"Tan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"issue":"6","key":"3981_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-015-1964-2","volume":"27","author":"BM Aslahi-Shahri","year":"2016","unstructured":"Aslahi-Shahri BM, Rahmani R, Chizari M, Maralani A, Eslami M, Golkar MJ, Ebrahimi A (2016) A hybrid method consisting of GA and SVM for intrusion detection system. Neural Comput Appl 27(6):1\u20138","journal-title":"Neural Comput Appl"},{"key":"3981_CR2","volume-title":"Pattern recognition and machine learning (Information science and statistics)","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning (Information science and statistics). Springer, New York"},{"issue":"20","key":"3981_CR3","first-page":"3813","volume":"178","author":"G Bloch","year":"2008","unstructured":"Bloch G, Lauer F, Colin G, Chamaillard Y (2008) Support vector regression from simulation data and few experimental samples. Inf Sci Int J 178(20):3813\u20133827","journal-title":"Inf Sci Int J"},{"issue":"8","key":"3981_CR4","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1162\/089976602760128081","volume":"14","author":"C Chang","year":"2002","unstructured":"Chang C, Lin C (2002) Training v-support vector regression: theory and algorithms. Neural Comput 14(8):1959\u20131977","journal-title":"Neural Comput"},{"issue":"2014","key":"3981_CR5","first-page":"1","volume":"2014","author":"J Chen","year":"2014","unstructured":"Chen J, Xue X, Ha M, Yu D (2014) Support vector regression method for wind speed prediction incorporating probability prior knowledge. Math Probl Eng 2014(2014):1\u201310","journal-title":"Math Probl Eng"},{"issue":"1","key":"3981_CR6","doi-asserted-by":"publisher","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"},{"issue":"1024\u2013123X","key":"3981_CR7","first-page":"16","volume":"1024","author":"T Farooq","year":"2010","unstructured":"Farooq T, Guergachi A, Krishnan S (2010) Knowledge-based Green\u2019s kernel for support vector regression. Math Probl Eng 1024(1024\u2013123X):16","journal-title":"Math Probl Eng"},{"issue":"1","key":"3981_CR8","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s00366-016-0453-2","volume":"33","author":"M Hasanipanah","year":"2017","unstructured":"Hasanipanah M, Shahnazar A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO\u2013SVR model. Eng Comput 33(1):23\u201331","journal-title":"Eng Comput"},{"issue":"1","key":"3981_CR9","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10994-007-5035-5","volume":"70","author":"F Lauer","year":"2008","unstructured":"Lauer F (2008) Incorporating prior knowledge in support vector regression. Mach Learn 70(1):89\u2013118","journal-title":"Mach Learn"},{"issue":"7","key":"3981_CR10","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1016\/j.neucom.2007.04.010","volume":"71","author":"F Lauer","year":"2008","unstructured":"Lauer F, Bloch G (2008) Incorporating prior knowledge in support vector machines for classification: a review. Neurocomputing 71(7):1578\u20131594","journal-title":"Neurocomputing"},{"issue":"3","key":"3981_CR11","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1016\/j.engstruct.2008.11.017","volume":"31","author":"AYT Leung","year":"2009","unstructured":"Leung AYT, Zhang H (2009) Particle swarm optimization of tuned mass dampers. Eng Struct 31(3):715\u2013728","journal-title":"Eng Struct"},{"issue":"9","key":"3981_CR12","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1002\/eqe.811","volume":"37","author":"AYT Leung","year":"2010","unstructured":"Leung AYT, Zhang H, Cheng CC, Lee YY (2010) Particle swarm optimization of TMD by non-stationary base excitation during earthquake. Earthq Eng Struct Dyn 37(9):1223\u20131246","journal-title":"Earthq Eng Struct Dyn"},{"issue":"5","key":"3981_CR13","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/j.chaos.2004.11.095","volume":"25","author":"B Liu","year":"2005","unstructured":"Liu B, Wang L, Jin YH, Tang F, Huang DX (2005) Improved particle swarm optimization combined with chaos. Chaos Solitons Fractals 25(5):1261\u20131271","journal-title":"Chaos Solitons Fractals"},{"issue":"1","key":"3981_CR14","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.asoc.2008.03.007","volume":"9","author":"Z Lu","year":"2009","unstructured":"Lu Z, Sun J (2009) Non-mercer hybrid kernel for linear programming support vector regression in nonlinear systems identification. Appl Soft Comput 9(1):94\u201399","journal-title":"Appl Soft Comput"},{"issue":"5","key":"3981_CR15","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TCYB.2013.2279834","volume":"44","author":"Z Lu","year":"2014","unstructured":"Lu Z, Sun J, Butts K (2014) Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification. IEEE Trans Cybern 44(5):712","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"3981_CR16","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1109\/TMC.2011.141","volume":"11","author":"S Milner","year":"2012","unstructured":"Milner S, Davis C, Zhang H, Llorca J (2012) Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trans Mobile Comput 11(7):1207\u20131222","journal-title":"IEEE Trans Mobile Comput"},{"issue":"3","key":"3981_CR17","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.apr.2015.10.022","volume":"7","author":"S Moazami","year":"2016","unstructured":"Moazami S, Noori R, Amiri BJ, Yeganeh B, Partani S, Safavi S (2016) Reliable prediction of carbon monoxide using developed support vector machine. Atmos Pollut Res 7(3):412\u2013418","journal-title":"Atmos Pollut Res"},{"issue":"2","key":"3981_CR18","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1002\/ep.10317","volume":"28","author":"R Noori","year":"2008","unstructured":"Noori R, Abdoli M, Ghasrodashti AA, Jalili Ghazizade M (2008) Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of mashhad. Environ Prog Sustain Energy 28(2):249\u2013258","journal-title":"Environ Prog Sustain Energy"},{"issue":"1","key":"3981_CR19","doi-asserted-by":"publisher","first-page":"04015039","DOI":"10.1061\/(ASCE)HY.1943-7900.0001062","volume":"142","author":"R Noori","year":"2016","unstructured":"Noori R, Deng Z, Kiaghadi A, Kachoosangi FT (2016) How reliable are ANN, ANFIS, and SVM techniques for predicting longitudinal dispersion coefficient in natural rivers? J Hydraul Eng 142(1):04015039","journal-title":"J Hydraul Eng"},{"issue":"3","key":"3981_CR20","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.jhydrol.2011.02.021","volume":"401","author":"R Noori","year":"2011","unstructured":"Noori R, Karbassi A, Moghaddamnia A, Han D, Zokaei-Ashtiani M, Farokhnia A, Gousheh MG (2011) Assessment of input variables determination on the SVM model performance using PCA, gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 401(3):177\u2013189","journal-title":"J Hydrol"},{"key":"3981_CR21","first-page":"185","volume-title":"Advances in kernel methods, chap 12.","author":"JC Platt","year":"1999","unstructured":"Platt JC (1999) Fast training of support vector machines using sequential minimal optimization. In: Sch\u00f6lkopf B, Burges C, Smola A (eds) Advances in kernel methods, chap 12. MIT press, Cambridge, MA, pp 185\u2013208"},{"issue":"15","key":"3981_CR22","doi-asserted-by":"publisher","first-page":"4361","DOI":"10.1007\/s00500-016-2067-4","volume":"21","author":"JS Sartakhti","year":"2017","unstructured":"Sartakhti JS, Afrabandpey H, Saraee M (2017) Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification. Soft Comput 21(15):4361\u20134373","journal-title":"Soft Comput"},{"issue":"5","key":"3981_CR23","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B Sch\u00f6lkopf","year":"2000","unstructured":"Sch\u00f6lkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207\u20131245","journal-title":"Neural Comput"},{"key":"3981_CR24","unstructured":"Smola A, Scholkopf B, Ratsch G (1999) Linear programs for automatic accuracy control in regression. In: Ninth international conference on artificial neural networks, 1999. ICANN 99, vol 2, pp 575\u2013580"},{"issue":"3","key":"3981_CR25","doi-asserted-by":"publisher","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":"3981_CR26","first-page":"409","volume":"38","author":"VN Vapnik","year":"1997","unstructured":"Vapnik VN (1997) The nature of statistical learning theory. IEEE Trans Neural Netw 38(4):409","journal-title":"IEEE Trans Neural Netw"},{"issue":"10","key":"3981_CR27","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.1177\/0954405415612371","volume":"231","author":"Y Wang","year":"2017","unstructured":"Wang Y, Jiang P (2017) Fluctuation evaluation and identification model for small-batch multistage machining processes of complex aircraft parts. Proc Inst Mech Eng Part B J Eng Manuf 231(10):1820\u20131837","journal-title":"Proc Inst Mech Eng Part B J Eng Manuf"},{"key":"3981_CR28","first-page":"1","volume":"1","author":"W Zhang","year":"2018","unstructured":"Zhang W, Yu L, Yoshida T, Wang Q (2018) Feature weighted confidence to incorporate prior knowledge into support vector machines for classification. Knowl Inf Syst 1:1\u201327","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"3981_CR29","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1109\/TR.2016.2515581","volume":"65","author":"W Zhao","year":"2016","unstructured":"Zhao W, Tao T, Zio E, Wang W (2016) A novel hybrid method of parameters tuning in support vector regression for reliability prediction: particle swarm optimization combined with analytical selection. IEEE Trans Reliab 65(3):1393\u20131405","journal-title":"IEEE Trans Reliab"},{"issue":"7","key":"3981_CR30","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.3390\/s17071692","volume":"17","author":"Y Zhao","year":"2017","unstructured":"Zhao Y, Jiang P (2017) Angular rate sensing with gyrowheel using genetic algorithm optimized neural networks. Sensors 17(7):1692","journal-title":"Sensors"},{"issue":"1","key":"3981_CR31","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/j.neucom.2005.12.128","volume":"70","author":"D Zheng","year":"2006","unstructured":"Zheng D, Wang J, Zhao Y (2006) Non-flat function estimation with a multi-scale support vector regression. Neurocomputing 70(1):420\u2013429","journal-title":"Neurocomputing"},{"issue":"7\u20138","key":"3981_CR32","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1007\/s00521-013-1418-7","volume":"24","author":"J Zhou","year":"2014","unstructured":"Zhou J, Duan B, Huang J, Cao H (2014) Data-driven modeling and optimization for cavity filters using linear programming support vector regression. Neural Comput Appl 24(7\u20138):1771\u20131783","journal-title":"Neural Comput Appl"},{"issue":"7","key":"3981_CR33","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1007\/s00500-014-1390-x","volume":"19","author":"J Zhou","year":"2015","unstructured":"Zhou J, Duan B, Huang J, Li N (2015) Incorporating prior knowledge and multi-kernel into linear programming support vector regression. Soft Comput 19(7):2047\u20132061","journal-title":"Soft Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-03981-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-03981-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-03981-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T19:22:56Z","timestamp":1577992976000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-03981-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,3]]},"references-count":33,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["3981"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-03981-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,3]]},"assertion":[{"value":"1 August 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}