{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T10:10:15Z","timestamp":1784887815729,"version":"3.55.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2017,12,16]],"date-time":"2017-12-16T00:00:00Z","timestamp":1513382400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Social Science Foundation of China","award":["13BJY098"],"award-info":[{"award-number":["13BJY098"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1007\/s00521-017-3296-x","type":"journal-article","created":{"date-parts":[[2017,12,16]],"date-time":"2017-12-16T04:45:24Z","timestamp":1513399524000},"page":"1425-1444","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Prediction of stock prices based on LM-BP neural network and the estimation of overfitting point by RDCI"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8752-8315","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fulin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyu","family":"Chi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiongya","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,12,16]]},"reference":[{"issue":"9","key":"3296_CR1","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.physleta.2013.01.006","volume":"377","author":"JC Li","year":"2013","unstructured":"Li JC, Mei DC (2013) The risks and returns of stock investment in a financial market. Phys Lett A 377(9):663\u2013670","journal-title":"Phys Lett A"},{"issue":"1","key":"3296_CR2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.knosys.2013.07.023","volume":"58","author":"KY Shen","year":"2014","unstructured":"Shen KY, Yan MR, Tzeng GH (2014) Combining VIKOR-DANP model for glamor stock selection and stock performance improvement. Knowl Based Syst 58(1):86\u201397","journal-title":"Knowl Based Syst"},{"issue":"1","key":"3296_CR3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ins.2011.11.036","volume":"256","author":"HV Pham","year":"2014","unstructured":"Pham HV, Cooper EW, Cao T, Kamei K (2014) Hybrid Kansei-SOM model using risk management and company assessment for stock trading. Inf Sci 256(1):8\u201324","journal-title":"Inf Sci"},{"key":"3296_CR4","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ins.2014.09.038","volume":"294","author":"MY Chen","year":"2015","unstructured":"Chen MY, Chen BT (2015) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci 294:227\u2013241","journal-title":"Inf Sci"},{"issue":"1","key":"3296_CR5","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10479-014-1779-z","volume":"234","author":"Y Liu","year":"2015","unstructured":"Liu Y, Chen Y, Wu S, Peng G, Lv B (2015) Composite leading search index: a preprocessing method of internet search data for stock trends prediction. Ann Oper Res 234(1):77\u201394","journal-title":"Ann Oper Res"},{"issue":"1","key":"3296_CR6","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.ins.2014.04.034","volume":"285","author":"J Smailovi\u0107","year":"2014","unstructured":"Smailovi\u0107 J, Gr\u010dar M, Lavra\u010d N, \u017dnidar\u0161ic M (2014) Stream-based active learning for sentiment analysis in the financial domain. Inf Sci 285(1):181\u2013203","journal-title":"Inf Sci"},{"issue":"30\u201331","key":"3296_CR7","first-page":"1997","volume":"378","author":"JC Li","year":"2014","unstructured":"Li JC, Li C, Mei DC (2014) Effects of time delay on stochastic resonance of the stock prices in financial system. Phys Lett A 378(30\u201331):1997\u20132000","journal-title":"Phys Lett A"},{"issue":"6","key":"3296_CR8","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1057\/palgrave.jors.2602442","volume":"59","author":"NCP Edirisinghe","year":"2008","unstructured":"Edirisinghe NCP, Zhang X (2008) Portfolio selection under DEA-based relative financial strength indicators: case of US industries. J Oper Res Soc 59(6):842\u2013856","journal-title":"J Oper Res Soc"},{"issue":"10","key":"3296_CR9","first-page":"1","volume":"10","author":"R Sumantyo","year":"2013","unstructured":"Sumantyo R, Melati (2013) Effect analysis of fundamental factors toward cigarettes company\u2019s stock price that listed in Indonesia Stock Exchange (IDX) period 2008\u20132013. Soc Sci Electron Publishing 10(10):1\u201320","journal-title":"Soc Sci Electron Publishing"},{"issue":"2","key":"3296_CR10","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.21275\/v5i2.NOV161462","volume":"5","author":"C Murugesan","year":"2016","unstructured":"Murugesan C, Sakthi Priya E (2016) Investment in stock market: fundamental and technical analysis. Int J Sci Res (IJSR) 5(2):1986\u20131991","journal-title":"Int J Sci Res (IJSR)"},{"issue":"11","key":"3296_CR11","first-page":"24","volume":"7","author":"YC Wang","year":"2014","unstructured":"Wang YC, Yu J, Wen SY (2014) Does fundamental and technical analysis reduce investment risk for growth stock? An analysis of Taiwan stock market. Int Bus Res 7(11):24\u201334","journal-title":"Int Bus Res"},{"key":"3296_CR12","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.techfore.2002.11.001","volume":"72","author":"SJ Lee","year":"2005","unstructured":"Lee SJ, Lee DJ, Oh HS (2005) Technological forecasting at the Korean stock market: a dynamic competition analysis using Lotka\u2013Volterra model. Technol Forecast Soc Change 72:1044\u20131057","journal-title":"Technol Forecast Soc Change"},{"issue":"3","key":"3296_CR13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0040-1625(99)00046-3","volume":"62","author":"T Modis","year":"1999","unstructured":"Modis T (1999) Technological forecasting at the stock market. Technol Forecast Soc Change 62(3):173\u2013202","journal-title":"Technol Forecast Soc Change"},{"issue":"4","key":"3296_CR14","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/0099-3964(70)90011-6","volume":"1","author":"AP Carter","year":"1970","unstructured":"Carter AP (1970) Technological forecasting and input\u2013output analysis. Technol Forecast 1(4):331\u2013345","journal-title":"Technol Forecast"},{"key":"3296_CR15","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.physa.2016.03.028","volume":"456","author":"HS Zhang","year":"2016","unstructured":"Zhang HS, Shen XY, Huang JP (2016) Pattern of trends in stock markets as revealed by the renormalization method. Phys A 456:340\u2013346","journal-title":"Phys A"},{"issue":"2","key":"3296_CR16","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.asoc.2012.09.024","volume":"13","author":"A Kazem","year":"2013","unstructured":"Kazem A, Sharifi E, Hussain FK, Saberi M, Hussain OK (2013) Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl Soft Comput 13(2):947\u2013958","journal-title":"Appl Soft Comput"},{"key":"3296_CR17","volume-title":"Time series analysis: forecasting and control","author":"GEP Box","year":"1994","unstructured":"Box GEP, Jenkins GM (1994) Time series analysis: forecasting and control, 3rd edn. Prentice Hall, Englewood Cliffs","edition":"3"},{"issue":"6","key":"3296_CR18","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.omega.2004.07.024","volume":"33","author":"P-F Pai","year":"2005","unstructured":"Pai P-F, Lin C-S (2005) A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33(6):497\u2013505","journal-title":"Omega"},{"issue":"11","key":"3296_CR19","doi-asserted-by":"crossref","first-page":"14346","DOI":"10.1016\/j.eswa.2011.04.222","volume":"38","author":"J-Z Wang","year":"2011","unstructured":"Wang J-Z, Wang J-J, Zhang Z-G, Guo S-P (2011) Forecasting stock indices with back propagation neural network. Expert Syst Appl 38(11):14346\u201314355","journal-title":"Expert Syst Appl"},{"issue":"12","key":"3296_CR20","doi-asserted-by":"crossref","first-page":"7908","DOI":"10.1016\/j.eswa.2010.04.045","volume":"37","author":"MA Boyacioglu","year":"2010","unstructured":"Boyacioglu MA, Avci D (2010) An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange. Expert Syst Appl 37(12):7908\u20137912","journal-title":"Expert Syst Appl"},{"issue":"9","key":"3296_CR21","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1016\/j.ins.2010.01.014","volume":"180","author":"C-H Cheng","year":"2010","unstructured":"Cheng C-H, Chen T-L, Wei L-Y (2010) A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf Sci 180(9):1610\u20131629","journal-title":"Inf Sci"},{"issue":"8","key":"3296_CR22","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.knosys.2010.05.004","volume":"23","author":"E Hadavandi","year":"2010","unstructured":"Hadavandi E, Shavandi H, Ghanbari A (2010) Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowl Based Syst 23(8):800\u2013808","journal-title":"Knowl Based Syst"},{"issue":"14","key":"3296_CR23","doi-asserted-by":"crossref","first-page":"6235","DOI":"10.1016\/j.eswa.2014.04.003","volume":"41","author":"A Bagheri","year":"2014","unstructured":"Bagheri A, Mohammadi Peyhani H, Akbari M (2014) Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Syst Appl 41(14):6235\u20136250","journal-title":"Expert Syst Appl"},{"issue":"3","key":"3296_CR24","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0165-0114(95)00220-0","volume":"81","author":"S-M Chen","year":"1996","unstructured":"Chen S-M (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81(3):311\u2013319","journal-title":"Fuzzy Sets Syst"},{"issue":"2","key":"3296_CR25","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1016\/j.eswa.2006.12.021","volume":"34","author":"C Cheng","year":"2008","unstructured":"Cheng C, Chen T, Teoh H, Chiang C (2008) Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Syst Appl 34(2):1126\u20131132","journal-title":"Expert Syst Appl"},{"issue":"3\u20134","key":"3296_CR26","first-page":"609","volume":"349","author":"H-K Yu","year":"2005","unstructured":"Yu H-K (2005) Weighted fuzzy time series models for TAIEX forecasting. Phys A 349(3\u20134):609\u2013624","journal-title":"Phys A"},{"issue":"1","key":"3296_CR27","first-page":"45","volume":"1","author":"X Liu","year":"2012","unstructured":"Liu X, Ma X (2012) Based on BP neural network stock prediction. J Curric Teach 1(1):45\u201350","journal-title":"J Curric Teach"},{"issue":"03","key":"3296_CR28","first-page":"1","volume":"4","author":"AS Babu","year":"2015","unstructured":"Babu AS, Reddy SK (2015) Exchange Rate Forecasting using ARIMA, neural network and fuzzy neuron. J Stock Forex Trad 4(03):1\u20135","journal-title":"J Stock Forex Trad"},{"issue":"12","key":"3296_CR29","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5120\/ijca2015905681","volume":"124","author":"A Murkute","year":"2015","unstructured":"Murkute A, Sarode T (2015) Forecasting market price of stock using artificial neural network. IJCA 124(12):11\u201315","journal-title":"IJCA"},{"issue":"04","key":"3296_CR30","doi-asserted-by":"crossref","first-page":"115","DOI":"10.4236\/ojapps.2015.54012","volume":"05","author":"Q Ye","year":"2015","unstructured":"Ye Q, Wei L (2015) The prediction of stock price based on improved wavelet neural network. Open J Appl Sci 05(04):115\u2013120","journal-title":"Open J Appl Sci"},{"key":"3296_CR31","first-page":"1543","volume":"303\u2013306","author":"XC Guo","year":"2013","unstructured":"Guo XC, Shang SH (2013) BP neural network research based on three convergence improved LM algorithm. Appl Mech Mater 303\u2013306:1543\u20131546","journal-title":"Appl Mech Mater"},{"key":"3296_CR32","first-page":"293","volume":"241\u2013244","author":"SH Liu","year":"2012","unstructured":"Liu SH, Bi ZJ, Zhang W (2012) A radar fault prediction based on LM-BP neural network. Appl Mech Mater 241\u2013244:293\u2013297","journal-title":"Appl Mech Mater"},{"key":"3296_CR33","doi-asserted-by":"crossref","unstructured":"Tan S, An Y, Wu Y, Zhang D (2016) Electromyography based handwriting recognition system using LM-BP Neural Network. In: 9th international conference on human system interactions (HSI)","DOI":"10.1109\/HSI.2016.7529613"},{"key":"3296_CR34","doi-asserted-by":"crossref","unstructured":"Li F (2014) Research on prediction model of stock price based on LM-BP neural network. In: Proceedings of the international conference on logistics, engineering, management and computer science","DOI":"10.2991\/lemcs-14.2014.177"},{"issue":"2","key":"3296_CR35","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1162\/neco.1992.4.2.141","volume":"4","author":"R Battiti","year":"1992","unstructured":"Battiti R (1992) First- and second-order methods for learning: between steepest descent and Newton\u2019s method. Neural Comput 4(2):141\u2013166","journal-title":"Neural Comput"},{"issue":"6","key":"3296_CR36","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","volume":"5","author":"MT Hagan","year":"1994","unstructured":"Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989\u2013993","journal-title":"IEEE Trans Neural Netw"},{"key":"3296_CR37","volume-title":"Neural network design","author":"MT Hagan","year":"1996","unstructured":"Hagan MT, Demuth HB, Beale MH, De Jes\u00fas O (1996) Neural network design, vol 20. PWS publishing company, Boston"},{"issue":"1","key":"3296_CR38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","volume":"43","author":"IA Basheer","year":"2000","unstructured":"Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3\u201331","journal-title":"J Microbiol Methods"},{"issue":"3","key":"3296_CR39","doi-asserted-by":"crossref","first-page":"6580","DOI":"10.1016\/j.eswa.2008.07.064","volume":"36","author":"L Zhang","year":"2009","unstructured":"Zhang L, Luo J, Yang S (2009) Forecasting box office revenue of movies with BP neural network. Expert Syst Appl 36(3):6580\u20136587","journal-title":"Expert Syst Appl"},{"issue":"149","key":"3296_CR40","first-page":"7431","volume":"9","author":"EN Pereira","year":"2015","unstructured":"Pereira EN, Scarpin CT, Albino L, Teixeira J (2015) Hybrid wavelet model for time series prediction. Appl Math Sci 9(149):7431\u20137438","journal-title":"Appl Math Sci"},{"issue":"8","key":"3296_CR41","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1002\/for.2366","volume":"34","author":"DAG Kolsrud","year":"2015","unstructured":"Kolsrud DAG (2015) A time-simultaneous prediction box for a multivariate time series. J Forecast 34(8):675\u2013693","journal-title":"J Forecast"},{"issue":"5","key":"3296_CR42","doi-asserted-by":"crossref","first-page":"052909","DOI":"10.1103\/PhysRevE.91.052909","volume":"91","author":"J Runge","year":"2015","unstructured":"Runge J, Donner RV, Kurths J (2015) Optimal model-free prediction from multivariate time series. Phys. Rev. E 91(5):052909","journal-title":"Phys. Rev. E"},{"key":"3296_CR43","doi-asserted-by":"crossref","first-page":"523","DOI":"10.4028\/www.scientific.net\/AMM.781.523","volume":"781","author":"W Sangjun","year":"2015","unstructured":"Sangjun W, Supakwong S, Thajchayapong S (2015) Prediction of financial time-series signals using \u00e1 Trous Wavelet Transform. Appl Mech Mater 781:523\u2013526","journal-title":"Appl Mech Mater"},{"issue":"2","key":"3296_CR44","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.annepidem.2014.10.015","volume":"25","author":"X Zhang","year":"2015","unstructured":"Zhang X, Pang Y, Cui M, Stallones L, Xiang H (2015) Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Ann Epidemiol 25(2):101\u2013106","journal-title":"Ann Epidemiol"},{"issue":"2","key":"3296_CR45","doi-asserted-by":"crossref","first-page":"47","DOI":"10.11648\/j.sjams.20150302.14","volume":"3","author":"MM Wasseja","year":"2015","unstructured":"Wasseja MM, Mwenda SN (2015) Analysis of the volatility of the electricity price in Kenya using autoregressive integrated moving average model. Sci J Appl Math Stat 3(2):47\u201357","journal-title":"Sci J Appl Math Stat"},{"issue":"12","key":"3296_CR46","doi-asserted-by":"crossref","first-page":"e008491","DOI":"10.1136\/bmjopen-2015-008491","volume":"5","author":"Y Lin","year":"2015","unstructured":"Lin Y, Chen M, Chen G, Wu X, Lin T (2015) Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China. BMJ Open 5(12):e008491","journal-title":"BMJ Open"},{"issue":"1","key":"3296_CR47","doi-asserted-by":"crossref","first-page":"188","DOI":"10.2166\/ws.2014.104","volume":"15","author":"HS Kang","year":"2015","unstructured":"Kang HS, Kim H, Lee J, Lee I, Kwak BY, Im H (2015) Optimization of pumping schedule based on water demand forecasting using a combined model of autoregressive integrated moving average and exponential smoothing. Water Sc Technol Water Supply 15(1):188\u2013195","journal-title":"Water Sc Technol Water Supply"},{"issue":"7","key":"3296_CR48","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/S0893-6080(03)00006-6","volume":"16","author":"Z Zhang","year":"2003","unstructured":"Zhang Z, Ma X, Yangb Y (2003) Bounds on the number of hidden neurons in three-layer binary neural networks. Neural Netw 16(7):995\u20131002","journal-title":"Neural Netw"},{"issue":"3","key":"3296_CR49","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s00521-009-0321-8","volume":"19","author":"X Liang","year":"2010","unstructured":"Liang X, Chen RC (2010) A unified mathematical form for removing neurons based on orthogonal projection and crosswise propagation. Neural Comput Appl 19(3):445\u2013457","journal-title":"Neural Comput Appl"},{"issue":"3","key":"3296_CR50","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0893-6080(89)90003-8","volume":"2","author":"KI Funahashi","year":"1989","unstructured":"Funahashi KI (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2(3):183\u2013192","journal-title":"Neural Netw"},{"issue":"8","key":"3296_CR51","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1002\/nag.291","volume":"27","author":"CG Chua","year":"2003","unstructured":"Chua CG, Goh ATC (2003) A hybrid Bayesian back-propagation neural network approach to multivariate modeling. Int J Numer Anal Methods Geomech 27(8):651\u2013667","journal-title":"Int J Numer Anal Methods Geomech"},{"issue":"5","key":"3296_CR52","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/31.31313","volume":"36","author":"G Mirchandani","year":"1989","unstructured":"Mirchandani G, Cao W (1989) On hidden nodes for neural nets. IEEE Trans Circuits Syst 36(5):661\u2013664","journal-title":"IEEE Trans Circuits Syst"},{"issue":"6","key":"3296_CR53","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1016\/0031-3203(94)90170-8","volume":"27","author":"I-C Jou","year":"1994","unstructured":"Jou I-C, You S-S, Chang L-W (1994) Analysis of hidden nodes for multi-layer perceptron neural networks. Pattern Recognit 27(6):859\u2013864","journal-title":"Pattern Recognit"},{"key":"3296_CR54","doi-asserted-by":"crossref","unstructured":"Sequin CH, Clay RD (1990) Fault tolerance in artificial neural networks. In: 1990 IJCNN international joint conference on neural networks","DOI":"10.1109\/IJCNN.1990.137651"},{"key":"3296_CR55","first-page":"31","volume":"631\u2013632","author":"J Jia","year":"2014","unstructured":"Jia J (2014) Financial time series prediction based on BP neural network. Appl Mech Mater 631\u2013632:31\u201334","journal-title":"Appl Mech Mater"},{"key":"3296_CR56","doi-asserted-by":"crossref","unstructured":"Yu S, Ou J (2009) Forecasting model of agricultural products prices in wholesale markets based on combined BP neural network-time series model. In: 2009 international conference on information management, innovation management and industrial engineering","DOI":"10.1109\/ICIII.2009.140"},{"key":"3296_CR57","doi-asserted-by":"crossref","unstructured":"Liang L, Shao F (2010) The study on short-time wind speed prediction based on time-series neural network algorithm. In: 2010 Asia-Pacific power and energy engineering conference","DOI":"10.1109\/APPEEC.2010.5448388"},{"issue":"12","key":"3296_CR58","first-page":"52","volume":"3","author":"S Yang","year":"2015","unstructured":"Yang S, Berdine G (2015) Model selection and model over-fitting. SWRCCC 3(12):52\u201355","journal-title":"SWRCCC"},{"key":"3296_CR59","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.ins.2015.04.037","volume":"317","author":"LV Utkin","year":"2015","unstructured":"Utkin LV, Wiencierz A (2015) Improving over-fitting in ensemble regression by imprecise probabilities. Inf Sci 317:315\u2013328","journal-title":"Inf Sci"},{"key":"3296_CR60","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.aca.2015.04.045","volume":"880","author":"BC Deng","year":"2015","unstructured":"Deng BC, Yun YH, Liang YZ, Cao DS, Xu QS, Yi LZ, Huang X (2015) A new strategy to prevent over-fitting in partial least squares models based on model population analysis. Anal Chim Acta 880:32\u201341","journal-title":"Anal Chim Acta"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-3296-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3296-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3296-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T18:07:04Z","timestamp":1660154824000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-3296-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,16]]},"references-count":60,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018,9]]}},"alternative-id":["3296"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-3296-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,16]]}}}