{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:31:36Z","timestamp":1753885896454,"version":"3.37.3"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s00521-020-05234-6","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T01:02:26Z","timestamp":1596502946000},"page":"4229-4241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Selecting data adaptive learner from multiple deep learners using Bayesian networks"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1529-9343","authenticated-orcid":false,"given":"Shusuke","family":"Kobayashi","sequence":"first","affiliation":[]},{"given":"Susumu","family":"Shirayama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,4]]},"reference":[{"key":"5234_CR1","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyperparameter optimization. J Mach Learn Res 13:281\u2013305","journal-title":"J Mach Learn Res"},{"key":"5234_CR2","unstructured":"Loshchilov I, Hutter F (2016) CMA-ES for hyperparameter optimization of deep neural networks. CoRR"},{"key":"5234_CR3","doi-asserted-by":"crossref","unstructured":"Lorenzo PR, Nalepa J, Kawulok M, Ramos LS, Pastor JR (2017) Particle swarm optimization for hyper- parameter selection in deep neural networks. In: Proceedings of the genetic and evolutionary computation conference. ACM, pp 481\u2013488","DOI":"10.1145\/3071178.3071208"},{"key":"5234_CR4","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, pp 2951\u20132959"},{"key":"5234_CR5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2013.03.047","volume":"137","author":"T Kuremoto","year":"2014","unstructured":"Kuremoto T, Kimura S, Kobayashi K, Obayashi M (2014) Time-series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137:47\u201356","journal-title":"Neurocomputing"},{"key":"5234_CR6","doi-asserted-by":"crossref","unstructured":"Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems, pp 153\u2013160","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"5234_CR7","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"1","key":"5234_CR8","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TASL.2011.2134090","volume":"20","author":"GE Dahl","year":"2012","unstructured":"Dahl GE, Yu D, Deng L, Acero A (2012) Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing 20(1):30\u201342","journal-title":"IEEE Transactions on audio, speech, and language processing"},{"key":"5234_CR9","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.apenergy.2016.11.111","volume":"188","author":"HZ Wang","year":"2017","unstructured":"Wang HZ, Li GQ, Wang GB, Peng JC, Jiang H, Liu YT (2017) Deep learning based ensemble approach for probabilistic wind power forecasting. Appl Energy 188:56\u201370","journal-title":"Appl Energy"},{"key":"5234_CR10","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","volume":"37","author":"HI Suk","year":"2017","unstructured":"Suk HI, Lee SW, Shen D, Alzheimerer\u2019s (2017) Disease neuroimaging initiative deep ensemble learning of sparse regression models for brain disease diagnosis. Med Image Anal 3:101\u2013113","journal-title":"Med Image Anal"},{"key":"5234_CR11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.eneco.2017.05.023","volume":"66","author":"Y Zhao","year":"2017","unstructured":"Zhao Y, Li J, Yu L (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Econ 66:9\u201316","journal-title":"Energy Econ"},{"key":"5234_CR12","doi-asserted-by":"crossref","unstructured":"Takahashi Y, Asada M (1999) Behavior acquisition by multi-layered reinforcement learning. In: Proceedings of 1999 IEEE international conference on systems, man, and cybernetics, pp 716\u2013721","DOI":"10.1109\/ICSMC.1999.816639"},{"issue":"1","key":"5234_CR13","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacob","year":"1991","unstructured":"Jacob RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixture of local experts. Neural Comput 3(1):79\u201387","journal-title":"Neural Comput"},{"issue":"10","key":"5234_CR14","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TNN.2011.2161999","volume":"22","author":"H Zhang","year":"2011","unstructured":"Zhang H, Liu G, Chow TWS, Liu W (2011) Textual and visual content-based anti-phishing: a Bayesian approach. IEEE Trans Neural Netw 22(10):1532\u20131546","journal-title":"IEEE Trans Neural Netw"},{"key":"5234_CR15","first-page":"115","volume":"5","author":"S Kobayashi","year":"2017","unstructured":"Kobayashi S, Shirayama S (2017) Time series forecasting with multiple deep learners: selection from a Bayesian network. J Data Anal Inf Process 5:115\u2013130","journal-title":"J Data Anal Inf Process"},{"key":"5234_CR16","doi-asserted-by":"crossref","unstructured":"Nomiya H, Uehara K (2007) Multistrategical image classification for image data mining. In: Proceedings of international workshop on multimedia data mining, pp 22\u201330","DOI":"10.1145\/1341920.1341926"},{"key":"5234_CR17","doi-asserted-by":"crossref","unstructured":"Takahashi Y, Takeda M, Asada M (1999) Continuous valued Q-learning for vision-guided behavior acquisition. In: Proceedings of 1999 IEEE\/SICE\/RSJ international conference on multisensor fusion and integration for intelligent systems, pp 255\u2013260","DOI":"10.1109\/MFI.1999.815999"},{"issue":"5","key":"5234_CR18","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1162\/089976602753633402","volume":"14","author":"R Collobert","year":"2002","unstructured":"Collobert R, Bengio S, Bengio Y (2002) A parallel mixture of SVMs for very large scale problems. Neural Comput 14(5):1105\u20131114","journal-title":"Neural Comput"},{"key":"5234_CR19","unstructured":"Tresp V (2000) Mixture of Gaussian processes. In: Proceedings of the 13th international conference on neural information proceeding system, pp 633\u2013639"},{"key":"5234_CR20","unstructured":"Theis L, Bethge M (2015) Generative image modeling using spatial LSTMs. In: Proceedings of the 28th international conference on neural information proceeding system, pp 1927\u20131935"},{"key":"5234_CR21","unstructured":"Deisenroth MP, Ng JW (2015) Distributed Gaussian processes. In: Proceedings of the 32nd international conference on international conference on machine learning, pp 1481\u20131490"},{"key":"5234_CR22","first-page":"1829","volume":"10","author":"B Shahbaba","year":"2009","unstructured":"Shahbaba B, Neal R (2009) Nonlinear models using Dirichlet process mixtures. J Mach Learn Res 10:1829\u20131850","journal-title":"J Mach Learn Res"},{"key":"5234_CR23","unstructured":"Eigen D, Ranzato MA, Sutskever I (2004) Learning factored representations in a deep mixture of experts. In: Workshop proceedings of the international conference on learning representations"},{"key":"5234_CR24","unstructured":"Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le Q, Hinton G, Dean J (2017) Outrageously large neural networks: the sparsely-gated mixture-of-experts layer. In: Conference proceedings of the international conference on learning representations"},{"key":"5234_CR25","doi-asserted-by":"crossref","unstructured":"Gross S, Gross S, Ranzato M, Szlam A (2017) Hard mixtures of experts for large scale weakly supervised vision. In: 2017 IEEE conference on computer vision and pattern recognition, pp 5085\u20135093","DOI":"10.1109\/CVPR.2017.540"},{"key":"5234_CR26","unstructured":"Pelleg D, Moore A (2000) X-means: extending K-means with efficient estimation of the number of clusters. In: Proceedings of 7th international conference on machine learning, pp 727\u2014734"},{"key":"5234_CR27","doi-asserted-by":"crossref","unstructured":"Geiger D, Heckerman D (1994) Learning Gaussian networks. In: Tenth conference on uncertainty in artificial intelligence, pp 235\u2013243","DOI":"10.1016\/B978-1-55860-332-5.50035-3"},{"key":"5234_CR28","doi-asserted-by":"crossref","unstructured":"Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Proceedings of the annual conference of international speech communication association, pp 338\u2013342","DOI":"10.21437\/Interspeech.2014-80"},{"key":"5234_CR29","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1007\/s11222-019-09857-1","volume":"29","author":"M Scutari","year":"2019","unstructured":"Scutari M, Vitolo C, Tucker A (2019) Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation. Stat Comput 29:1095\u20131108","journal-title":"Stat Comput"},{"issue":"3","key":"5234_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v035.i03","volume":"35","author":"M Scutari","year":"2010","unstructured":"Scutari M (2010) Learning Bayesian networks with the bnlearn R Package. J Stat Softw 35(3):1\u201322","journal-title":"J Stat Softw"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05234-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05234-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05234-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T07:15:31Z","timestamp":1696490131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05234-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,4]]},"references-count":30,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["5234"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05234-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2020,8,4]]},"assertion":[{"value":"18 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 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"}}]}}