{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:10:31Z","timestamp":1761581431452,"version":"3.41.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2016,6,4]],"date-time":"2016-06-04T00:00:00Z","timestamp":1464998400000},"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":["61375065","61432014"],"award-info":[{"award-number":["61375065","61432014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61432012"],"award-info":[{"award-number":["61432012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2017,11]]},"DOI":"10.1007\/s00500-016-2205-z","type":"journal-article","created":{"date-parts":[[2016,6,4]],"date-time":"2016-06-04T04:31:55Z","timestamp":1465014715000},"page":"6471-6479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Finding a good initial configuration of parameters for restricted Boltzmann machine pre-training"],"prefix":"10.1007","volume":"21","author":[{"given":"Chunzhi","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiancheng","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojie","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,6,4]]},"reference":[{"issue":"1","key":"2205_CR1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0893-6080(89)90014-2","volume":"2","author":"P Baldi","year":"1988","unstructured":"Baldi P, Hornik K (1988) Neural networks and principal component analysis: learning from examples without local minima. Neural Netw 2(1):53\u201358","journal-title":"Neural Netw"},{"doi-asserted-by":"crossref","unstructured":"Bengio Y (2013) Deep learning of representations: looking forward. In: SLSP, pp 1\u201337","key":"2205_CR2","DOI":"10.1007\/978-3-642-36657-4_1"},{"issue":"8","key":"2205_CR3","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell (PAMI) 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell (PAMI)"},{"unstructured":"Bengio Y, Delalleau O (2011) On the expressive power of deep architectures. In: ALT, pp 18\u201336","key":"2205_CR4"},{"unstructured":"Chen D, Socher R, Manning CD, Ng AY (2013) Learning new facts from knowledge bases with neural tensor networks and semantic word vectors. In: ICLR","key":"2205_CR5"},{"unstructured":"Cho K, Raiko T, Ilin A (2011) Enhanced gradient and adaptive learning rate for training restricted Boltzmann machines. In: ICML, pp 105\u2013112","key":"2205_CR6"},{"doi-asserted-by":"crossref","unstructured":"Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: ICASSP, pp 8599\u20138603","key":"2205_CR7","DOI":"10.1109\/ICASSP.2013.6639344"},{"key":"2205_CR8","first-page":"625","volume":"11","author":"D Erhan","year":"2010","unstructured":"Erhan D, Bengio Y et al (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625\u2013660","journal-title":"J Mach Learn Res"},{"key":"2205_CR9","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1162\/NECO_a_00085","volume":"23","author":"A Fischer","year":"2011","unstructured":"Fischer A, Igel C (2011) Bounding the bias of contrastive divergence learning. Neural Comput 23:664\u2013673","journal-title":"Neural Comput"},{"issue":"1","key":"2205_CR10","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.patcog.2013.05.025","volume":"47","author":"A Fischer","year":"2014","unstructured":"Fischer A, Igel C (2014) Training restricted Boltzmann machines: an introduction. Pattern recognit 47(1):25\u201339","journal-title":"Pattern recognit"},{"doi-asserted-by":"crossref","unstructured":"Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771\u20131800","key":"2205_CR11","DOI":"10.1162\/089976602760128018"},{"doi-asserted-by":"crossref","unstructured":"Hinton GE (2012) A practical guide to training restricted Boltzmann machines. In: Montavon G, Orr GB, M\u00fcller K-R (eds) Neural networks: tricks of the trade (2nd edn). Springer, Berlin, Heidelberg, pp 599\u2013619","key":"2205_CR12","DOI":"10.1007\/978-3-642-35289-8_32"},{"issue":"6","key":"2205_CR13","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton G, Deng L, Yu D, Dahl G, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process Mag 29(6):82\u201397","journal-title":"IEEE Signal Process Mag"},{"issue":"5786","key":"2205_CR14","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov R (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"doi-asserted-by":"crossref","unstructured":"Huang P, He X, Gao J, Deng L, Acero A, Heck L (2013) Learning deep structured semantic models for web search using clickthrough data. In: CIKM, pp 2333\u20132338","key":"2205_CR15","DOI":"10.1145\/2505515.2505665"},{"unstructured":"Kamyshanska H, Memisevic R (2013) On autoencoder scoring. In: ICML, pp 720\u2013728","key":"2205_CR16"},{"doi-asserted-by":"crossref","unstructured":"Kohli P, Osokin A, Jegelka S (2013) A principled deep random field model for image segmentation. In: CVPR, pp 1971\u20131978","key":"2205_CR17","DOI":"10.1109\/CVPR.2013.257"},{"doi-asserted-by":"publisher","unstructured":"Liu J, Gong M, Zhao J et al (2014) Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft Comput 1\u201313. doi: 10.1007\/s00500-014-1460-0","key":"2205_CR18","DOI":"10.1007\/s00500-014-1460-0"},{"issue":"10\u201311","key":"2205_CR19","first-page":"1425","volume":"52","author":"JC Lv","year":"2006","unstructured":"Lv JC, Yi Z (2006) Global convergence of a PCA learning algorithm with a constant learning rate. Comput Math Appl 52(10\u201311):1425\u20131438","journal-title":"Comput Math Appl"},{"issue":"3","key":"2205_CR20","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/TNN.2007.891193","volume":"18","author":"JC Lv","year":"2007","unstructured":"Lv JC, Yi Z, Tan KK (2007) Determination of the number of principal directions in a biologically plausible PCA model. IEEE Trans Neural Netw 18(3):910\u2013916","journal-title":"IEEE Trans Neural Netw"},{"unstructured":"Lv JC, Yi Z, Zhou J (2010a) Subspace learning of neural networks. CRC Press","key":"2205_CR21"},{"issue":"1","key":"2205_CR22","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TFUZZ.2009.2038711","volume":"18","author":"JC Lv","year":"2010","unstructured":"Lv JC, Tan KK, Yi Z, Huang S (2010b) A family of fuzzy learning algorithms for robust principal component analysis neural networks. IEEE Trans Fuzzy Syst 18(1):217\u2013226","journal-title":"IEEE Trans Fuzzy Syst"},{"unstructured":"Luo P, Wang X, Tang X (2012) Hierarchical face parsing via deep learning. In: CVPR, pp 2480\u20132487","key":"2205_CR23"},{"unstructured":"Mittelman R, Kuipers B, Savarese S, Lee H (2014) Structured recurrent temporal restricted Boltzmann machine. In: ICML, pp 1647\u20131655","key":"2205_CR24"},{"doi-asserted-by":"crossref","unstructured":"Mohamed A, Dahl G, Hinton G (2012) Acoustic modeling using deep belief networks. IEEE Trans Audio Speech Lang Process 20(1):14\u201322","key":"2205_CR25","DOI":"10.1109\/TASL.2011.2109382"},{"doi-asserted-by":"crossref","unstructured":"Ranzato M, Hinton G (2010) Modeling pixel means and covariances using factorized third-order Boltzmann machines. In: CVPR, pp 2551\u20132558","key":"2205_CR26","DOI":"10.1109\/CVPR.2010.5539962"},{"doi-asserted-by":"crossref","unstructured":"Salakhutdinov R, Mnih A, Hinton GE (2007) Restricted Boltzmann machines for collaborative filtering. In: ICML, pp 791\u2013798","key":"2205_CR27","DOI":"10.1145\/1273496.1273596"},{"doi-asserted-by":"crossref","unstructured":"Salakhutdinov R, Murray I (2008) On the quantitative analysis of deep belief networks. In: ICML, pp 872\u2013879","key":"2205_CR28","DOI":"10.1145\/1390156.1390266"},{"doi-asserted-by":"crossref","unstructured":"Socher R, Perelygin A, Wu J, Chuang J, Manning C, Ng Andrew, Potts Chris (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP","key":"2205_CR29","DOI":"10.18653\/v1\/D13-1170"},{"doi-asserted-by":"crossref","unstructured":"Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. CVPR","key":"2205_CR30","DOI":"10.1109\/CVPR.2013.446"},{"unstructured":"Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: ICML, pp 1139\u20131147","key":"2205_CR31"},{"unstructured":"Tang Y, Salakhutdinov R, Hinton G (2012) Robust Boltzmann machines for recognition and denoising. In: CVPR","key":"2205_CR32"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-016-2205-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2205-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2205-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-016-2205-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T20:40:02Z","timestamp":1748983202000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-016-2205-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,4]]},"references-count":32,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2017,11]]}},"alternative-id":["2205"],"URL":"https:\/\/doi.org\/10.1007\/s00500-016-2205-z","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2016,6,4]]}}}