{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T15:40:14Z","timestamp":1705678814468},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s00500-020-04960-2","type":"journal-article","created":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T22:07:08Z","timestamp":1590790028000},"page":"16565-16576","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Perturbation-based classifier"],"prefix":"10.1007","volume":"24","author":[{"given":"Edson L.","family":"Ara\u00fajo","sequence":"first","affiliation":[]},{"given":"George D. C.","family":"Cavalcanti","sequence":"additional","affiliation":[]},{"given":"Tsang Ing","family":"Ren","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"4960_CR1","volume-title":"Theory of approximation","author":"NI Achieser","year":"2013","unstructured":"Achieser NI (2013) Theory of approximation. Courier Corporation, North Chelmsford"},{"issue":"4","key":"4960_CR2","doi-asserted-by":"publisher","first-page":"119","DOI":"10.5121\/ijdkp.2013.3408","volume":"3","author":"RR Ade","year":"2013","unstructured":"Ade RR, Deshmukh PR (2013) Methods for incremental learning: a survey. Int J Data Min Knowl Manage Process 3(4):119\u2013125","journal-title":"Int J Data Min Knowl Manage Process"},{"key":"4960_CR3","unstructured":"Bache K, Lichman M (2013) UCI Machine Learning Repository"},{"issue":"2","key":"4960_CR4","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJ Burges","year":"1998","unstructured":"Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):955\u2013974","journal-title":"Data Min Knowl Discov"},{"issue":"1","key":"4960_CR5","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1214\/ss\/1177010638","volume":"9","author":"B Cheng","year":"1994","unstructured":"Cheng B, Titterington DM (1994) Neural networks: a review from a statistical perspective. Stat Sci 9(1):2\u201354","journal-title":"Stat Sci"},{"issue":"4","key":"4960_CR6","first-page":"309","volume":"9","author":"GF Cooper","year":"1992","unstructured":"Cooper GF, Herskovits E (1992) A bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309\u2013347","journal-title":"Mach Learn"},{"issue":"1","key":"4960_CR7","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"2006","unstructured":"Cover T, Hart P (2006) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"},{"issue":"23","key":"4960_CR8","doi-asserted-by":"publisher","first-page":"7207","DOI":"10.1007\/s00500-016-2263-2","volume":"21","author":"RJ de Jesus","year":"2017","unstructured":"de Jesus RJ (2017) A method with neural networks for the classification of fruits and vegetables. Soft Comput 21(23):7207\u20137220","journal-title":"Soft Comput"},{"key":"4960_CR9","first-page":"1","volume":"07","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 07:1\u201330","journal-title":"J Mach Learn Res"},{"key":"4960_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0711-5","volume-title":"A probabilistic theory of pattern recognition","author":"L Devroye","year":"1996","unstructured":"Devroye L, Gyorfi L, Lugosi G (1996) A probabilistic theory of pattern recognition. Springer, Berlin"},{"issue":"20","key":"4960_CR11","doi-asserted-by":"publisher","first-page":"5919","DOI":"10.1007\/s00500-016-2269-9","volume":"21","author":"J Ding","year":"2017","unstructured":"Ding J, Wang H, Li C, Chai T, Wang J (2017) An online learning neural network ensembles with random weights for regression of sequential data stream. Soft Comput 21(20):5919\u20135937","journal-title":"Soft Comput"},{"key":"4960_CR12","volume-title":"Pattern classification and scene analysis","author":"RO Duda","year":"1973","unstructured":"Duda RO, Hart PE et al (1973) Pattern classification and scene analysis. Wiley, New York"},{"key":"4960_CR13","volume-title":"Pattern classification","author":"RO Duda","year":"2012","unstructured":"Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New York"},{"issue":"4","key":"4960_CR14","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/S0167-9473(01)00069-X","volume":"38","author":"T Evgeniou","year":"2002","unstructured":"Evgeniou T, Poggio T, Pontil M, Verri A (2002) Regularization and statistical learning theory for data analysis. Comput Stat Data Anal 38(4):421\u2013432","journal-title":"Comput Stat Data Anal"},{"key":"4960_CR15","doi-asserted-by":"crossref","unstructured":"Flores MJ, Gamez JA, Martinez AM, Puerta JM (2009) Gaode and haode: two proposals based on aode to deal with continuous variables. In: Proceedings of the 26th annual international conference on machine learning, pp 313\u2013320","DOI":"10.1145\/1553374.1553414"},{"issue":"2\u20133","key":"4960_CR16","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1023\/A:1007465528199","volume":"29","author":"N Friedman","year":"1997","unstructured":"Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2\u20133):131\u2013163","journal-title":"Mach Learn"},{"key":"4960_CR17","volume-title":"Introduction to statistical pattern recognition","author":"K Fukunaga","year":"1972","unstructured":"Fukunaga K (1972) Introduction to statistical pattern recognition, 1st edn. Academic Press, Orlando","edition":"1"},{"issue":"7","key":"4960_CR18","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1109\/34.506799","volume":"18","author":"JP Hoffbeck","year":"1996","unstructured":"Hoffbeck JP, Landgrebe DA (1996) Covariance matrix estimation and classification with limited training data. IEEE Trans Pattern Anal Mach Intell 18(7):763\u2013767","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"4960_CR19","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1109\/TNNLS.2013.2258937","volume":"24","author":"A Iosifidis","year":"2013","unstructured":"Iosifidis A, Tefas A, Pitas I (2013) On the optimal class representation in linear discriminant analysis. IEEE Trans Neural Netw Learn Syst 24(9):1491\u20131497","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"4960_CR20","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/34.824819","volume":"22","author":"AK Jain","year":"2000","unstructured":"Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4\u201337","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"4960_CR21","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1109\/TSP.2004.830991","volume":"52","author":"J Kivinen","year":"2004","unstructured":"Kivinen J, Smola AJ, Williamson RC (2004) Online learning with kernels. IEEE Trans Signal Process 52(8):2165\u20132176","journal-title":"IEEE Trans Signal Process"},{"issue":"15","key":"4960_CR22","doi-asserted-by":"publisher","first-page":"4447","DOI":"10.1007\/s00500-016-2447-9","volume":"21","author":"R Kumar","year":"2017","unstructured":"Kumar R, Srivastava S, Gupta J (2017) Modeling and adaptive control of nonlinear dynamical systems using radial basis function network. Soft Comput 21(15):4447\u20134463","journal-title":"Soft Comput"},{"issue":"4","key":"4960_CR23","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1109\/TGRS.2002.1006358","volume":"40","author":"BC Kuo","year":"2002","unstructured":"Kuo BC, Landgrebe DA (2002) A covariance estimator for small sample size classification problems and its application to feature extraction. IEEE Trans Geosci Remote Sens 40(4):814\u2013819","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"4960_CR24","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/S0047-259X(03)00096-4","volume":"88","author":"O Ledoit","year":"2004","unstructured":"Ledoit O, Wolf M (2004) A well-conditioned estimator for large-dimensional covariance matrices. J Multivar Anal 88:365\u2013411","journal-title":"J Multivar Anal"},{"issue":"23","key":"4960_CR25","doi-asserted-by":"publisher","first-page":"7053","DOI":"10.1007\/s00500-016-2247-2","volume":"21","author":"P Liu","year":"2017","unstructured":"Liu P, Choo KKR, Wang L, Huang F (2017) Svm or deep learning? A comparative study on remote sensing image classification. Soft Comput 21(23):7053\u20137065","journal-title":"Soft Comput"},{"issue":"3","key":"4960_CR26","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1134\/S1054661813030103","volume":"23","author":"A Lutz","year":"2013","unstructured":"Lutz A, Rodner E, Denzler J (2013) I want to know more\u2014efficient multi-class incremental learning using gaussian processes. Pattern Recognit Image Anal 23(3):402\u2013407","journal-title":"Pattern Recognit Image Anal"},{"key":"4960_CR27","unstructured":"Lutz A, Rodner E, Denzler J (2011) Efficient multi-class incremental learning using gaussian processes. In: Open German-Russian workshop on pattern recognition and image understanding, pp 182\u2013185"},{"key":"4960_CR28","volume-title":"Machine learning","author":"TM Mitchell","year":"1997","unstructured":"Mitchell TM (1997) Machine learning. McGraw-Hill, Boston"},{"issue":"1","key":"4960_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijar.2006.01.002","volume":"43","author":"A Perez","year":"2006","unstructured":"Perez A, Larranaga P, Inza I (2006) Supervised classification with conditional gaussian networks: increasing the structure complexity from naive bayes. Int J Approx Reason 43(1):1\u201325","journal-title":"Int J Approx Reason"},{"issue":"2","key":"4960_CR30","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ijar.2008.08.008","volume":"50","author":"A Perez","year":"2009","unstructured":"Perez A, Larranaga P, Inza I (2009) Bayesian classifiers based on kernel density estimation: flexible classifiers. Int J Approx Reason 50(2):341\u2013362","journal-title":"Int J Approx Reason"},{"issue":"1","key":"4960_CR31","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/0047-259X(92)90108-R","volume":"43","author":"F Perron","year":"1992","unstructured":"Perron F (1992) Minimax estimators of a covariance matrix. J Multivar Anal 43(1):16\u201328","journal-title":"J Multivar Anal"},{"key":"4960_CR32","volume-title":"Matrix algebra useful for statistics","author":"SR Searle","year":"1982","unstructured":"Searle SR (1982) Matrix algebra useful for statistics. Wiley, New York"},{"issue":"4","key":"4960_CR33","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1109\/36.774728","volume":"37","author":"S Tadjudin","year":"1999","unstructured":"Tadjudin S, Landgrebe DA (1999) Covariance estimation with limited training samples. IEEE Trans Geosci Remote Sens Sens 37(4):2113\u20132118","journal-title":"IEEE Trans Geosci Remote Sens Sens"},{"key":"4960_CR34","volume-title":"Pattern recognition","author":"S Theodoridis","year":"2008","unstructured":"Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press, California","edition":"4"},{"key":"4960_CR35","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.spl.2016.12.002","volume":"123","author":"WN van Wieringen","year":"2017","unstructured":"van Wieringen WN (2017) On the mean squared error of the ridge estimator of the covariance and precision matrix. Stat Probab Lett 123:88\u201392","journal-title":"Stat Probab Lett"},{"key":"4960_CR36","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/B978-0-444-53858-1.00008-9","volume-title":"Time series analysis: methods and applications, handbook of statistics","author":"WB Wu","year":"2012","unstructured":"Wu WB, Xiao H (2012) Covariance matrix estimation in time series. In: Tata Subba Rao SSR, Rao C (eds) Time series analysis: methods and applications, handbook of statistics, vol 30. Elsevier, Amsterdam pp, pp 187\u2013209"},{"issue":"21","key":"4960_CR37","doi-asserted-by":"publisher","first-page":"6407","DOI":"10.1007\/s00500-016-2199-6","volume":"21","author":"F Zhu","year":"2017","unstructured":"Zhu F, Yang J, Xu S, Gao C, Ye N, Yin T (2017) Incorporating neighbors distribution knowledge into support vector machines. Soft Comput 21(21):6407\u20136420","journal-title":"Soft Comput"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04960-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-020-04960-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04960-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:04:11Z","timestamp":1622246651000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-020-04960-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,29]]},"references-count":37,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["4960"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-04960-2","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,29]]},"assertion":[{"value":"29 May 2020","order":1,"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"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights statement"}}]}}