{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:21:22Z","timestamp":1774797682983,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T00:00:00Z","timestamp":1644451200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T00:00:00Z","timestamp":1644451200000},"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":["Mobile Netw Appl"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s11036-022-01913-x","type":"journal-article","created":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T00:02:41Z","timestamp":1644451361000},"page":"928-935","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Comparative Research of Hyper-Parameters Mathematical Optimization Algorithms for Automatic Machine Learning in New Generation Mobile Network"],"prefix":"10.1007","volume":"27","author":[{"given":"Xiaohang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengren","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,10]]},"reference":[{"key":"1913_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68913-5","volume-title":"Derivative-free and blackbox optimization","author":"C Audet","year":"2017","unstructured":"Audet C, Hare W (2017) Derivative-free and blackbox optimization. Springer, Cham"},{"key":"1913_CR2","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281\u2013305","journal-title":"J Mach Learn Res"},{"key":"1913_CR3","unstructured":"Bergstra JS, et al. (2011) Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems 24. Curran Associates, Inc. 2546\u20132554"},{"key":"1913_CR4","unstructured":"Falkner S, Klein A, Hutter, F (2018) Practical hyper-parameter optimization for deep learning. in ICLR 2018 Workshop."},{"key":"1913_CR5","first-page":"962","volume":"28","author":"M Feurer","year":"2015","unstructured":"Feurer M et al (2015) Efficient and robust automated machine learning. Adv Neural Inf Process Syst 28:962\u20132970","journal-title":"Adv Neural Inf Process Syst"},{"key":"1913_CR6","unstructured":"Lorraine J, Duvenaud D (2018) Stochastic hyper-parameter optimization through hypernetworks. CoRR, abs\/1802.0"},{"key":"1913_CR7","unstructured":"Li L, et al. (2016) Hyperband: A novel bandit-based approach to hyper-parameter optimization, eprint  arXiv:1603.06560"},{"key":"1913_CR8","doi-asserted-by":"crossref","unstructured":"Thornton C, et al. (2013) Auto-WEKA: combined selection and hyper-parameter optimization of classification algorithms, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM (KDD' 13) 847\u2013855","DOI":"10.1145\/2487575.2487629"},{"key":"1913_CR9","unstructured":"Ghanbari H, Scheinberg K (2017) Black-box optimization in machine learning with trust region based derivative free algorithm, CoRR, abs\/1703.0.2017"},{"key":"1913_CR10","doi-asserted-by":"crossref","unstructured":"Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration, in Coello LION 5, Rome, Italy, C. A. C. B. T.-L. and I. O. 5th I. C. (ed.). Berlin, Heidelberg: Springer Berlin Heidelberg 507\u2013523","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"1913_CR11","first-page":"2951","volume":"25","author":"J Snoek","year":"2012","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 25:2951\u20132959","journal-title":"Adv Neural Inf Process Syst"},{"key":"1913_CR12","first-page":"4134","volume":"29","author":"JT Springenberg","year":"2016","unstructured":"Springenberg JT et al (2016) Bayesian optimization with robust bayesian neural networks. Adv Neural Inf Process Syst 29:4134\u20134142","journal-title":"Adv Neural Inf Process Syst"},{"key":"1913_CR13","first-page":"2004","volume":"29","author":"K Swersky","year":"2013","unstructured":"Swersky K, Snoek J, Adams RP (2013) Advances in neural information processing systems 26. Adv Neural Inf Process Syst 29:2004\u20132012","journal-title":"Adv Neural Inf Process Syst"},{"key":"1913_CR14","unstructured":"Swersky K, Snoek J, Adams RP (2014) Freeze-thaw bayesian optimization. CoRR, abs\/1406.3. 2014"},{"key":"1913_CR15","unstructured":"Maclaurin D, Duvenaud D, Adams R (2015) Gradient-based hyper-parameter optimization through reversible learning. Proceedings of the 32nd International Conference on Machine Learning 2113\u20132122"},{"key":"1913_CR16","unstructured":"Pedregosa F (2016) Hyper-parameter optimization with approximate gradient. Proceedings of The 33rd International Conference on Machine Learning. New York, New York, USA: PMLR (Proceedings of Machine Learning Research). 737\u2013746"},{"key":"1913_CR17","unstructured":"Franceschi L et al. (2017) Forward and reverse gradient-based hyper-parameter optimization. Proceedings of the 34th International Conference on Machine Learning. International Convention Centre, Sydney, Australia: PMLR (Proceedings of Machine Learning Research). 1165\u20131173"},{"key":"1913_CR18","unstructured":"Klein A et al. (2016) Fast Bayesian optimization of machine learning hyper-parameters on large datasets. CoRR, abs\/1605.0.2016."},{"key":"1913_CR19","unstructured":"Kotthoff L, et al. (2017) Auto-WEKA 2.0: Automatic model selection and hyper-parameter optimization in WEKA. J Mach Learn Res 18(25): 1\u20135"},{"key":"1913_CR20","unstructured":"Yogatama D, Mann G (2014) Efficient transfer learning method for automatic hyper-parameter tuning, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics 1077\u20131085"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-022-01913-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-022-01913-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-022-01913-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T14:10:59Z","timestamp":1657980659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-022-01913-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,10]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["1913"],"URL":"https:\/\/doi.org\/10.1007\/s11036-022-01913-x","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,10]]},"assertion":[{"value":"13 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}