{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:08Z","timestamp":1740108068599,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2017,2,10]],"date-time":"2017-02-10T00:00:00Z","timestamp":1486684800000},"content-version":"unspecified","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":["61375045"],"award-info":[{"award-number":["61375045"]}],"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":[[2018,5]]},"DOI":"10.1007\/s00521-017-2864-4","type":"journal-article","created":{"date-parts":[[2017,2,10]],"date-time":"2017-02-10T09:16:22Z","timestamp":1486718182000},"page":"401-412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Training neural networks by marginalizing out hidden layer noise"],"prefix":"10.1007","volume":"29","author":[{"given":"Yanjun","family":"Li","sequence":"first","affiliation":[]},{"given":"Ping","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,2,10]]},"reference":[{"key":"2864_CR1","doi-asserted-by":"crossref","unstructured":"Li YJ, Xin X, Guo P (2015) Neural networks with marginalized corrupted hidden layer. In: Proceedings of international conference on neural information processing, pp 506\u2013514","DOI":"10.1007\/978-3-319-26555-1_57"},{"key":"2864_CR2","unstructured":"Burges CJC, Sch\u00f6lkopf B (1997) Improving the accuracy and speed of support vector machines. In: Advances in neural information processing systems, pp 375\u2013381"},{"issue":"6088","key":"2864_CR3","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"issue":"11","key":"2864_CR4","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TNN.2010.2073482","volume":"21","author":"BM Wilamowski","year":"2010","unstructured":"Wilamowski BM, Yu H (2010) Neural network learning without backpropagation. IEEE Trans Neural Netw 21(11):1793\u20131803","journal-title":"IEEE Trans Neural Netw"},{"issue":"6","key":"2864_CR5","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":"2864_CR6","unstructured":"Branke J (1995) Evolutionary algorithms for neural network design and training. In: Proceedings of the first nordic workshop on genetic algorithms and its applications"},{"key":"2864_CR7","volume-title":"Principles of neurodynamics: perceptrons and the theory of brain mechanisms","author":"F Rosenblatt","year":"1962","unstructured":"Rosenblatt F (1962) Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan Books, New York"},{"issue":"3\u20134","key":"2864_CR8","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s00521-013-1522-8","volume":"25","author":"S Ding","year":"2014","unstructured":"Ding S, Xu X, Nie R (2014) Extreme learning machine and its applications. Neural Comput Appl 25(3\u20134):549\u2013556","journal-title":"Neural Comput Appl"},{"key":"2864_CR9","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S0925-2312(03)00385-0","volume":"56","author":"P Guo","year":"2004","unstructured":"Guo P, Lyu MR (2004) A pseudoinverse learning algorithm for feedforward neural networks with stacked generalization applications to software reliability growth data. Neurocomputing 56:101\u2013121","journal-title":"Neurocomputing"},{"key":"2864_CR10","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"key":"2864_CR11","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"2864_CR12","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning, pp 513\u2013520"},{"key":"2864_CR13","unstructured":"Maillet F, Eck D, Desjardins G, Lamere P (2009) Steerable playlist generation by learning song similarity from radio station playlists. In: International society for music information retrieval conference, pp 345\u2013350"},{"key":"2864_CR14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.specom.2014.02.001","volume":"60","author":"B Xia","year":"2014","unstructured":"Xia B, Bao C (2014) Wiener filtering based speech enhancement with weighted denoising auto-encoder and noise classification. Speech Commun 60:13\u201329","journal-title":"Speech Commun"},{"key":"2864_CR15","unstructured":"Chen M, Xu Z, Weinberger K, Sha F (2012) Marginalized denoising autoencoders for domain adaptation. In: Proceedings of the 29th international conference on machine learning, pp 767\u2013774"},{"key":"2864_CR16","unstructured":"Maaten L, Chen M, Tyree S, Weinberger KQ (2013) Learning with marginalized corrupted features. In: Proceedings of the 30th international conference on machine learning, pp 410\u2013418"},{"key":"2864_CR17","unstructured":"Herbrich R, Graepel T (2004) Invariant pattern recognition by semidefinite programming machines. In: Advances in neural information processing systems, pp 33\u201340"},{"key":"2864_CR18","unstructured":"Teo CH, Globerson A, Roweis ST, Smola AJ (2007) Convex learning with invariances. In: Advances in neural information processing systems, pp 1489\u20131496"},{"key":"2864_CR19","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"key":"2864_CR20","unstructured":"Wager S, Wang S, Liang PS (2013) Dropout training as adaptive regularization. In: Advances in neural information processing systems, pp 351\u2013359"},{"key":"2864_CR21","unstructured":"Wang S, Manning C (2013) Fast dropout training. In: Proceedings of the 30th international conference on machine learning, pp 118\u2013126"},{"key":"2864_CR22","doi-asserted-by":"crossref","unstructured":"Qian Q, Hu J, Jin R, Pei J, Zhu S (2014) Distance metric learning using dropout: a structured regularization approach. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 323\u2013332","DOI":"10.1145\/2623330.2623678"},{"key":"2864_CR23","unstructured":"Lawrence ND, Sch\u00f6lkopf B (2001) Estimating a kernel fisher discriminant in the presence of label noise. In: Proceedings of the 18th international conference on machine learning, Citeseer, pp 306\u2013313"},{"key":"2864_CR24","unstructured":"Chen M, Zheng A, Weinberger K (2013) Fast image tagging. In: Proceedings of the 30th international conference on machine Learning, pp 1274\u20131282"},{"key":"2864_CR25","doi-asserted-by":"crossref","unstructured":"Li Y, Yang M, Xu Z, Zhang ZM (2016) Learning with marginalized corrupted features and labels together. In: Thirtieth AAAI conference on artificial intelligence, pp 1251\u20131257","DOI":"10.1609\/aaai.v30i1.10152"},{"issue":"16","key":"2864_CR26","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1016\/j.neucom.2007.02.009","volume":"70","author":"GB Huang","year":"2007","unstructured":"Huang GB, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16):3056\u20133062","journal-title":"Neurocomputing"},{"issue":"16","key":"2864_CR27","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1016\/j.neucom.2007.10.008","volume":"71","author":"GB Huang","year":"2008","unstructured":"Huang GB, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16):3460\u20133468","journal-title":"Neurocomputing"},{"key":"2864_CR28","volume-title":"Pattern classification","author":"RO Duda","year":"2012","unstructured":"Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, Hoboken"},{"key":"2864_CR29","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/00401706.1974.10489157","volume":"16","author":"DM Allen","year":"1974","unstructured":"Allen DM (1974) The relationship between variable selection and data agumentation and a method for prediction. Technometrics 16:125\u2013127","journal-title":"Technometrics"},{"issue":"3","key":"2864_CR30","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JAK Suykens","year":"1999","unstructured":"Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293\u2013300","journal-title":"Neural Process Lett"},{"issue":"5439","key":"2864_CR31","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531\u2013537","journal-title":"Science"},{"key":"2864_CR32","unstructured":"Blake CL, Merz CJ (1998) UCI repository of machine learning databases. http:\/\/archive.ics.uci.edu\/ml\/datasets.html"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-2864-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2864-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-2864-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T23:13:51Z","timestamp":1658618031000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-2864-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,10]]},"references-count":32,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,5]]}},"alternative-id":["2864"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-2864-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2017,2,10]]}}}