{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T13:31:50Z","timestamp":1758893510358},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,6,25]],"date-time":"2016-06-25T00:00:00Z","timestamp":1466812800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2018,2]]},"DOI":"10.1007\/s10044-016-0566-7","type":"journal-article","created":{"date-parts":[[2016,6,25]],"date-time":"2016-06-25T03:00:03Z","timestamp":1466823603000},"page":"67-80","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A fast BP networks with dynamic sample selection for handwritten recognition"],"prefix":"10.1007","volume":"21","author":[{"given":"Qi","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daqi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,6,25]]},"reference":[{"key":"566_CR1","first-page":"113","volume":"1","author":"EL Allwein","year":"2001","unstructured":"Allwein EL, Schapire RE, Singer Y (2001) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113\u2013141","journal-title":"J Mach Learn Res"},{"issue":"1","key":"566_CR2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/72.363444","volume":"6","author":"R Anand","year":"1995","unstructured":"Anand R, Mehrotra K, Mohan CK, Ranka S (1995) Efficient classification for multiclass problems using modular neural networks. IEEE Trans Neural Netw 6(1):117\u2013124","journal-title":"IEEE Trans Neural Netw"},{"key":"566_CR3","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. School of Information and Computer Sciences, University of California, Irvine. http:\/\/archive.ics.uci.edu\/ml"},{"key":"566_CR4","doi-asserted-by":"crossref","unstructured":"Bottou L, Cortes C, Denker JS, Drucker I, Guyon LD, Jackel Y, LeCun UA, Muller E, Sackinger P, Simard et\u00a0al (1994) Comparison of classifier methods: a case study in handwritten digit recognition. In: International conference on pattern recognition. IEEE, pp 77\u201377","DOI":"10.1109\/ICPR.1994.576879"},{"issue":"3","key":"566_CR5","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"1","key":"566_CR6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"TM Cover","year":"1967","unstructured":"Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"},{"key":"566_CR7","unstructured":"Gao D, Xie C, Nie G (2003) Combinative neural-network-based classifiers for optical handwritten character and letter recognition. In: International joint conference on neural networks, vol 3, pp 2232\u20132237"},{"key":"566_CR8","unstructured":"Gao D, Zhu S, Gu W (2005) A modular single-hidden-layer perceptron for letter recognition. In: Artificial neural networks: biological inspirations. Springer, Berlin, pp 461\u2013467"},{"issue":"3","key":"566_CR9","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.dsp.2009.10.004","volume":"20","author":"G Dede","year":"2010","unstructured":"Dede G, Sazl\u0131 MH (2010) Speech recognition with artificial neural networks. Digit Signal Proc 20(3):763\u2013768","journal-title":"Digit Signal Proc"},{"issue":"2","key":"566_CR10","first-page":"161","volume":"6","author":"PW Frey","year":"1991","unstructured":"Frey PW, Slate DJ (1991) Letter recognition using holland-style adaptive classifiers. Mach Learn 6(2):161\u2013182","journal-title":"Mach Learn"},{"issue":"4","key":"566_CR11","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2012","unstructured":"Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C Appl Rev 42(4):463\u2013484","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"1","key":"566_CR12","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0893-6080(88)90023-8","volume":"1","author":"RP Gorman","year":"1988","unstructured":"Gorman RP, Sejnowski TJ (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":"566_CR13","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"1998","unstructured":"Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River","edition":"2"},{"key":"566_CR14","unstructured":"Huang FJ, LeCun Y (2006) Large-scale learning with SVM and convolutional for generic object categorization. In: IEEE computer society conference on computer vision and pattern recognition, vol\u00a01, pp 284\u2013291"},{"key":"566_CR15","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1106\u20131114"},{"issue":"1","key":"566_CR16","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1109\/TNN.2008.2005830","volume":"19","author":"K Labusch","year":"2008","unstructured":"Labusch K, Barth E, Martinetz T (2008) Simple method for high-performance digit recognition based on sparse coding. IEEE Trans Neural Netw 19(1):1985\u20131991","journal-title":"IEEE Trans Neural Netw"},{"issue":"11","key":"566_CR17","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","journal-title":"Proc IEEE"},{"key":"566_CR18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"566_CR19","unstructured":"Li D, Dong Y (2011) Deep convex network: a scalable architecture for speech pattern classification. In: Interspeech. International Speech Communication Association, pp 2285\u20132288"},{"issue":"3","key":"566_CR20","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/72.286925","volume":"5","author":"VV Phansalkar","year":"1994","unstructured":"Phansalkar VV, Sastry PS (1994) Analysis of the back-propagation algorithm with momentum. IEEE Trans Neural Netw 5(3):505\u2013506","journal-title":"IEEE Trans Neural Netw"},{"key":"566_CR21","volume-title":"C4.5: Programs for machine learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, Los Altos"},{"issue":"10","key":"566_CR22","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1109\/TNN.2011.2163169","volume":"22","author":"S Razavi","year":"2011","unstructured":"Razavi S, Tolson BA (2011) A new formulation for feedforward neural networks. IEEE Trans Neural Netw 22(10):1588\u20131598","journal-title":"IEEE Trans Neural Netw"},{"key":"566_CR23","first-page":"101","volume":"5","author":"R Rifkin","year":"2004","unstructured":"Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101\u2013141","journal-title":"J Mach Learn Res"},{"issue":"9","key":"566_CR24","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE (1986) Learning representations by back-propagating errors. Nature 323(9):533\u2013536","journal-title":"Nature"},{"key":"566_CR25","doi-asserted-by":"crossref","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Technical report","DOI":"10.21236\/ADA164453"},{"issue":"3","key":"566_CR26","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Li F (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"566_CR27","first-page":"412","volume":"2","author":"R Salakhutdinov","year":"2007","unstructured":"Salakhutdinov R, Hinton GE (2007) Learning a nonlinear embedding by preserving class neighbourhood structure. J Mach Learn Res 2:412\u2013419","journal-title":"J Mach Learn Res"},{"issue":"12","key":"566_CR28","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1162\/089976601317098565","volume":"13","author":"R Setiono","year":"2001","unstructured":"Setiono R (2001) Feedforward neural network construction using cross validation. Neural Comput 13(12):2865\u20132877","journal-title":"Neural Comput"},{"issue":"5","key":"566_CR29","first-page":"73","volume":"3","author":"V Shrivastava","year":"2012","unstructured":"Shrivastava V, Sharma N (2012) Artificial neural network based optical character recognition. Signal Image Process 3(5):73\u201380","journal-title":"Signal Image Process"},{"key":"566_CR30","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.neunet.2012.04.013","volume":"33","author":"J Wang","year":"2012","unstructured":"Wang J, Wu W, Zurada JM (2012) Computational properties and convergence analysis of BPNN for cyclic and almost cyclic learning with penalty. Neural Netw 33:127\u2013135","journal-title":"Neural Netw"},{"issue":"1","key":"566_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"B Yoshua","year":"2009","unstructured":"Yoshua B (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1\u2013127","journal-title":"Found Trends Mach Learn"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10044-016-0566-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-016-0566-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-016-0566-7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-016-0566-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T01:26:20Z","timestamp":1568078780000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10044-016-0566-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,25]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,2]]}},"alternative-id":["566"],"URL":"https:\/\/doi.org\/10.1007\/s10044-016-0566-7","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,25]]}}}