{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T23:53:33Z","timestamp":1773186813384,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T00:00:00Z","timestamp":1603238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T00:00:00Z","timestamp":1603238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"crossref","award":["PID2019-104966GB-I00"],"award-info":[{"award-number":["PID2019-104966GB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003086","name":"Eusko Jaurlaritza","doi-asserted-by":"publisher","award":["IT1244-19"],"award-info":[{"award-number":["IT1244-19"]}],"id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003086","name":"Eusko Jaurlaritza","doi-asserted-by":"publisher","award":["ELKARTEK"],"award-info":[{"award-number":["ELKARTEK"]}],"id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003086","name":"Eusko Jaurlaritza","doi-asserted-by":"publisher","award":["BERC 2018-2021"],"award-info":[{"award-number":["BERC 2018-2021"]}],"id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"crossref","award":["SEV-2017-0718"],"award-info":[{"award-number":["SEV-2017-0718"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05419-z","type":"journal-article","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T17:03:51Z","timestamp":1603299831000},"page":"6575-6594","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["In-depth analysis of SVM kernel learning and its components"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3574-6681","authenticated-orcid":false,"given":"Ibai","family":"Roman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1005-8535","authenticated-orcid":false,"given":"Roberto","family":"Santana","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7271-1931","authenticated-orcid":false,"given":"Alexander","family":"Mendiburu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4683-8111","authenticated-orcid":false,"given":"Jose A.","family":"Lozano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"issue":"1","key":"5419_CR1","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.neucom.2006.03.004","volume":"70","author":"S Ali","year":"2006","unstructured":"Ali S, Smith-Miles KA (2006) A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing 70(1):173\u2013186. https:\/\/doi.org\/10.1016\/j.neucom.2006.03.004","journal-title":"Neurocomputing"},{"key":"5419_CR2","doi-asserted-by":"publisher","unstructured":"Alizadeh M, Ebadzadeh MM (2011) Kernel evolution for support vector classification. In: 2011 IEEE workshop on evolving and adaptive intelligent systems (EAIS), pp 93\u201399. https:\/\/doi.org\/10.1109\/EAIS.2011.5945924","DOI":"10.1109\/EAIS.2011.5945924"},{"key":"5419_CR3","doi-asserted-by":"publisher","unstructured":"Bing W, Wen-qiong Z, Ling C, Jia-hong L (2010) A GP-based kernel construction and optimization method for RVM. In: 2010 the 2nd international conference on computer and automation engineering (ICCAE), vol 4, pp 419\u2013423. https:\/\/doi.org\/10.1109\/ICCAE.2010.5451646","DOI":"10.1109\/ICCAE.2010.5451646"},{"key":"5419_CR4","doi-asserted-by":"publisher","unstructured":"Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory. ACM, New York, NY, USA, COLT \u201992, pp 144\u2013152. https:\/\/doi.org\/10.1145\/130385.130401. (Event-place: Pittsburgh, Pennsylvania, USA)","DOI":"10.1145\/130385.130401"},{"key":"5419_CR5","unstructured":"Burges CJ, Crisp DJ (2000) Uniqueness of the SVM solution. In: Advances in neural information processing systems, pp 223\u2013229"},{"key":"5419_CR6","unstructured":"Chapelle O (2002) Support vector machines: induction principle, adaptive tuning and prior knowledge. Ph.D. thesis, LIP6"},{"key":"5419_CR7","unstructured":"Cho Y, Saul LK (2009) Kernel methods for deep learning. In: Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A (eds) Advances in neural information processing systems, vol 22. Curran Associates, Inc., pp 342\u2013350. http:\/\/papers.nips.cc\/paper\/3628-kernel-methods-for-deep-learning.pdf"},{"key":"5419_CR8","first-page":"265","volume":"2","author":"K Crammer","year":"2001","unstructured":"Crammer K, Singer Y (2001) On the algorithmic implementation of multiclass kernel-based vector machines. J Mach Learn Res 2:265\u2013292","journal-title":"J Mach Learn Res"},{"key":"5419_CR9","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"5419_CR10","doi-asserted-by":"publisher","unstructured":"Dio\u015fan L, Rogozan A, Pecuchet JP (2007) Evolving kernel functions for SVMs by genetic programming. In: Sixth international conference on machine learning and applications (ICMLA 2007), pp 19\u201324. https:\/\/doi.org\/10.1109\/ICMLA.2007.70","DOI":"10.1109\/ICMLA.2007.70"},{"key":"5419_CR11","doi-asserted-by":"publisher","unstructured":"Dio\u015fan L, Rogozan A, Pecuchet JP (2008) Optimising multiple kernels for SVM by genetic programming. In: Evolutionary computation in combinatorial optimization, Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 230\u2013241. https:\/\/doi.org\/10.1007\/978-3-540-78604-7_20","DOI":"10.1007\/978-3-540-78604-7_20"},{"issue":"2","key":"5419_CR12","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/s10489-010-0260-1","volume":"36","author":"L Dio\u015fan","year":"2012","unstructured":"Dio\u015fan L, Rogozan A, Pecuchet JP (2012) Improving classification performance of support vector machine by genetically optimising kernel shape and hyper-parameters. Appl Intell 36(2):280\u2013294. https:\/\/doi.org\/10.1007\/s10489-010-0260-1","journal-title":"Appl Intell"},{"key":"5419_CR13","unstructured":"Dua D, Graff C (2017) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"3","key":"5419_CR14","doi-asserted-by":"publisher","first-page":"481","DOI":"10.5802\/afst.1342","volume":"21","author":"N Durrande","year":"2012","unstructured":"Durrande N, Ginsbourger D, Roustant O (2012) Additive covariance kernels for high-dimensional Gaussian process modeling. Annales de la Facult\u00e9 de Sciences de Toulouse Tome 21(3):481\u2013499","journal-title":"Annales de la Facult\u00e9 de Sciences de Toulouse Tome"},{"key":"5419_CR15","unstructured":"Duvenaud D (2014) Automatic model construction with Gaussian processes. Thesis. University of Cambridge. http:\/\/www.repository.cam.ac.uk\/handle\/1810\/247281"},{"key":"5419_CR16","unstructured":"Duvenaud D, Lloyd J, Grosse R, Tenenbaum J, Zoubin G (2013) Structure discovery in nonparametric regression through compositional kernel search. In: Proceedings of the 30th international conference on machine learning, pp 1166\u20131174. http:\/\/jmlr.org\/proceedings\/papers\/v28\/duvenaud13.html"},{"issue":"Jul","key":"5419_CR17","first-page":"2171","volume":"13","author":"FA Fortin","year":"2012","unstructured":"Fortin FA, Rainville FMD, Gardner MA, Parizeau M, Gagn\u00e9 C (2012) DEAP: evolutionary algorithms made easy. J Mach Learn Res 13(Jul):2171\u20132175","journal-title":"J Mach Learn Res"},{"issue":"200","key":"5419_CR18","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","journal-title":"J Am Stat Assoc"},{"key":"5419_CR19","doi-asserted-by":"publisher","unstructured":"Gagn\u00e9 C, Schoenauer M, Sebag M, Tomassini M (2006) Genetic programming for kernel-based learning with co-evolving subsets selection. In: Parallel problem solving from nature\u2014PPSN IX, Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 1008\u20131017. https:\/\/doi.org\/10.1007\/11844297_102","DOI":"10.1007\/11844297_102"},{"key":"5419_CR20","first-page":"299","volume":"2","author":"MG Genton","year":"2002","unstructured":"Genton MG (2002) Classes of kernels for machine learning: a statistics perspective. J Mach Learn Res 2:299\u2013312","journal-title":"J Mach Learn Res"},{"key":"5419_CR21","doi-asserted-by":"publisher","unstructured":"Gijsberts A, Metta G, Rothkrantz L (2010) Evolutionary optimization of least-squares support vector machines. In: Data mining, annals of information systems. Springer, Boston, MA, pp 277\u2013297. https:\/\/doi.org\/10.1007\/978-1-4419-1280-0_12","DOI":"10.1007\/978-1-4419-1280-0_12"},{"key":"5419_CR22","doi-asserted-by":"publisher","unstructured":"Girdea M, Ciortuz L (2007) A hybrid genetic programming and boosting technique for learning kernel functions from training data. In: Ninth international symposium on symbolic and numeric algorithms for scientific computing (SYNASC 2007), pp 395\u2013402. https:\/\/doi.org\/10.1109\/SYNASC.2007.71","DOI":"10.1109\/SYNASC.2007.71"},{"key":"5419_CR23","unstructured":"HajiGhassemi N, Deisenroth M (2014) Analytic long-term forecasting with periodic Gaussian processes. In: Proceedings of machine learning research, pp 303\u2013311. http:\/\/proceedings.mlr.press\/v33\/hajighassemi14.html"},{"issue":"3\u20134","key":"5419_CR24","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s10462-005-9009-3","volume":"24","author":"T Howley","year":"2005","unstructured":"Howley T, Madden MG (2005) The genetic kernel support vector machine: description and evaluation. Artif Intell Rev 24(3\u20134):379\u2013395. https:\/\/doi.org\/10.1007\/s10462-005-9009-3","journal-title":"Artif Intell Rev"},{"key":"5419_CR25","doi-asserted-by":"publisher","unstructured":"Howley T, Madden MG (2006) An evolutionary approach to automatic kernel construction. In: Artificial neural networks\u2014ICANN 2006, Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 417\u2013426. https:\/\/doi.org\/10.1007\/11840930_43","DOI":"10.1007\/11840930_43"},{"key":"5419_CR26","doi-asserted-by":"publisher","unstructured":"Hussain M, Wajid SK, Elzaart A, Berbar M (2011) A comparison of SVM kernel functions for breast cancer detection. In: Imaging and visualization 2011 eighth international conference computer graphics, pp 145\u2013150. https:\/\/doi.org\/10.1109\/CGIV.2011.31","DOI":"10.1109\/CGIV.2011.31"},{"key":"5419_CR27","unstructured":"Joachims T (1998) Making large-scale SVM learning practical. Technical report. https:\/\/www.econstor.eu\/handle\/10419\/77178"},{"issue":"3","key":"5419_CR28","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s12065-012-0073-8","volume":"5","author":"P Koch","year":"2012","unstructured":"Koch P, Bischl B, Flasch O, Bartz-Beielstein T, Weihs C, Konen W (2012) Tuning and evolution of support vector kernels. Evol Intell 5(3):153\u2013170. https:\/\/doi.org\/10.1007\/s12065-012-0073-8","journal-title":"Evol Intell"},{"key":"5419_CR29","volume-title":"Genetic programming: on the programming of computers by means of natural selection","author":"JR Koza","year":"1992","unstructured":"Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge"},{"key":"5419_CR30","doi-asserted-by":"publisher","unstructured":"Li CH, Lin CT, Kuo BC, Chu HS (2010) An automatic method for selecting the parameter of the RBF kernel function to support vector machines. In: 2010 IEEE international geoscience and remote sensing symposium, pp 836\u2013839. https:\/\/doi.org\/10.1109\/IGARSS.2010.5649251. (iSSN: 2153-7003)","DOI":"10.1109\/IGARSS.2010.5649251"},{"key":"5419_CR31","volume-title":"Kernel learning algorithms for face recognition","author":"JB Li","year":"2013","unstructured":"Li JB, Chu SC, Pan JS (2013) Kernel learning algorithms for face recognition. Springer, Berlin"},{"key":"5419_CR32","doi-asserted-by":"publisher","unstructured":"MacKay DJC (1996) Bayesian methods for backpropagation networks. In: Models of neural networks III, physics of neural networks. Springer, New York, NY, pp 211\u2013254. https:\/\/doi.org\/10.1007\/978-1-4612-0723-8_6","DOI":"10.1007\/978-1-4612-0723-8_6"},{"issue":"441\u2013458","key":"5419_CR33","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1098\/rsta.1909.0016","volume":"209","author":"J Mercer","year":"1909","unstructured":"Mercer J (1909) XVI. Functions of positive and negative type, and their connection the theory of integral equations. Philos Trans R Soc Lond Ser A Contain Pap Math Phys Character 209(441\u2013458):415\u2013446. https:\/\/doi.org\/10.1098\/rsta.1909.0016","journal-title":"Philos Trans R Soc Lond Ser A Contain Pap Math Phys Character"},{"issue":"2","key":"5419_CR34","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1007\/s10489-014-0533-1","volume":"41","author":"MA Mezher","year":"2014","unstructured":"Mezher MA, Abbod MF (2014) Genetic folding for solving multiclass SVM problems. Appl Intell 41(2):464\u2013472. https:\/\/doi.org\/10.1007\/s10489-014-0533-1","journal-title":"Appl Intell"},{"issue":"6","key":"5419_CR35","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/j.renene.2003.11.009","volume":"29","author":"MA Mohandes","year":"2004","unstructured":"Mohandes MA, Halawani TO, Rehman S, Hussain AA (2004) Support vector machines for wind speed prediction. Renew Energy 29(6):939\u2013947. https:\/\/doi.org\/10.1016\/j.renene.2003.11.009","journal-title":"Renew Energy"},{"key":"5419_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0745-0","volume-title":"Bayesian learning for neural networks. Lecture notes in statistics","author":"RM Neal","year":"1996","unstructured":"Neal RM (1996) Bayesian learning for neural networks. Lecture notes in statistics. Springer, New York"},{"issue":"1","key":"5419_CR37","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s13040-017-0154-4","volume":"10","author":"RS Olson","year":"2017","unstructured":"Olson RS, La Cava W, Orzechowski P, Urbanowicz RJ, Moore JH (2017) PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min 10(1):36. https:\/\/doi.org\/10.1186\/s13040-017-0154-4","journal-title":"BioData Min"},{"key":"5419_CR38","doi-asserted-by":"publisher","unstructured":"Pei Y (2019) Automatic decision making for parameters in kernel method. In: 2019 IEEE symposium series on computational intelligence (SSCI), pp 3207\u20133214. https:\/\/doi.org\/10.1109\/SSCI44817.2019.9002691","DOI":"10.1109\/SSCI44817.2019.9002691"},{"key":"5419_CR39","doi-asserted-by":"publisher","unstructured":"Phienthrakul T, Kijsirikul B (2007) GPES: an algorithm for evolving hybrid kernel functions of support vector machines. In: 2007 IEEE congress on evolutionary computation, pp 2636\u20132643. https:\/\/doi.org\/10.1109\/CEC.2007.4424803","DOI":"10.1109\/CEC.2007.4424803"},{"issue":"3","key":"5419_CR40","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large-Margin Classif 10(3):61\u201374","journal-title":"Adv Large-Margin Classif"},{"issue":"2","key":"5419_CR41","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1093\/comjnl\/7.2.155","volume":"7","author":"MJD Powell","year":"1964","unstructured":"Powell MJD (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7(2):155\u2013162. https:\/\/doi.org\/10.1093\/comjnl\/7.2.155","journal-title":"Comput J"},{"key":"5419_CR42","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.ins.2014.05.025","volume":"281","author":"H Pree","year":"2014","unstructured":"Pree H, Herwig B, Gruber T, Sick B, David K, Lukowicz P (2014) On general purpose time series similarity measures and their use as kernel functions in support vector machines. Inf Sci 281:478\u2013495. https:\/\/doi.org\/10.1016\/j.ins.2014.05.025","journal-title":"Inf Sci"},{"key":"5419_CR43","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.ins.2015.06.027","volume":"323","author":"T Reitmaier","year":"2015","unstructured":"Reitmaier T, Sick B (2015) The responsibility weighted Mahalanobis kernel for semi-supervised training of support vector machines for classification. Inf Sci 323:179\u2013198. https:\/\/doi.org\/10.1016\/j.ins.2015.06.027","journal-title":"Inf Sci"},{"key":"5419_CR44","unstructured":"Schuh MA, Angryk RA, Sheppard J (2012) Evolving kernel functions with particle swarms and genetic programming. In: Youngblood GM, McCarthy PM (eds) Proceedings of the twenty-fifth international Florida artificial intelligence research society conference, 2012. AAAI Press, Marco Island, Florida, pp 80\u201385. http:\/\/www.aaai.org\/ocs\/index.php\/FLAIRS\/FLAIRS12\/paper\/view\/4479\/4770.pdf"},{"issue":"2","key":"5419_CR45","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461\u2013464. https:\/\/doi.org\/10.1214\/aos\/1176344136","journal-title":"Ann Stat"},{"key":"5419_CR46","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1080\/01621459.1986.10478341","volume":"81","author":"JP Shaffer","year":"2012","unstructured":"Shaffer JP (2012) Modified sequentially rejective multiple test procedures. J Am Stat Assoc 81:826\u2013831","journal-title":"J Am Stat Assoc"},{"key":"5419_CR47","doi-asserted-by":"publisher","unstructured":"Sousa ADM, Lorena AC, Basgalupp MP (2017) GEEK: grammatical evolution for automatically evolving kernel functions. In: 2017 IEEE Trustcom\/BigDataSE\/ICESS, pp 941\u2013948. https:\/\/doi.org\/10.1109\/Trustcom\/BigDataSE\/ICESS.2017.334","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.334"},{"key":"5419_CR48","doi-asserted-by":"publisher","unstructured":"Sullivan KM, Luke S (2007) Evolving kernels for support vector machine classification. In: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, New York, NY, USA, GECCO \u201907, pp 1702\u20131707. https:\/\/doi.org\/10.1145\/1276958.1277292","DOI":"10.1145\/1276958.1277292"},{"key":"5419_CR49","doi-asserted-by":"publisher","unstructured":"Thadani K, Ashutosh, Jayaraman VK, Sundararajan V (2006) Evolutionary selection of kernels in support vector machines. In: 2006 international conference on advanced computing and communications, pp 19\u201324. https:\/\/doi.org\/10.1109\/ADCOM.2006.4289849","DOI":"10.1109\/ADCOM.2006.4289849"},{"key":"5419_CR50","unstructured":"Valerio R, Vilalta R (2014) Kernel selection in support vector machines using gram-matrix properties. In: Proceedings of the 27th international conference on advances in neural information processing systems. Workshop on modern nonparametrics: automating the learning pipeline, NIPS, vol 14, pp 2\u20134"},{"key":"5419_CR51","first-page":"774","volume":"24","author":"V Vapnik","year":"1963","unstructured":"Vapnik V (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774\u2013780","journal-title":"Autom Remote Control"},{"key":"5419_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin"},{"key":"5419_CR53","doi-asserted-by":"publisher","unstructured":"Zhang F (2011) Positive semidefinite matrices. In: Matrix theory, universitext. Springer, New York, NY, pp 199\u2013252. https:\/\/doi.org\/10.1007\/978-1-4614-1099-7_7","DOI":"10.1007\/978-1-4614-1099-7_7"},{"issue":"1","key":"5419_CR54","first-page":"138","volume":"10","author":"L Zhao","year":"2018","unstructured":"Zhao L, Gai M, Jia Y (2018) Classification of multiple power quality disturbances based on PSO-SVM of hybrid kernel function. J Inf Hiding Multimed Signal Process 10(1):138\u2013146","journal-title":"J Inf Hiding Multimed Signal Process"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05419-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05419-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05419-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T23:45:57Z","timestamp":1634773557000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05419-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,21]]},"references-count":54,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5419"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05419-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,21]]},"assertion":[{"value":"29 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}