{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T17:32:14Z","timestamp":1772818334321,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T00:00:00Z","timestamp":1563840000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T00:00:00Z","timestamp":1563840000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2016R1A2B1008994"],"award-info":[{"award-number":["2016R1A2B1008994"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"crossref","award":["R1623371"],"award-info":[{"award-number":["R1623371"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information & communications Technology Promotion","doi-asserted-by":"crossref","award":["2018-0-00440"],"award-info":[{"award-number":["2018-0-00440"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1007\/s10489-019-01519-z","type":"journal-article","created":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T12:02:47Z","timestamp":1563883367000},"page":"296-313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Robust expected model change for active learning in regression"],"prefix":"10.1007","volume":"50","author":[{"given":"Sung Ho","family":"Park","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2205-8516","authenticated-orcid":false,"given":"Seoung Bum","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,23]]},"reference":[{"issue":"4","key":"1519_CR1","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s10489-013-0491-z","volume":"40","author":"WJ Chen","year":"2014","unstructured":"Chen WJ, Shao YH, Xu DK, Fu YF (2014) Manifold proximal support vector machine for semi-supervised classification. Appl Intell 40(4):623\u2013638","journal-title":"Appl Intell"},{"issue":"10","key":"1519_CR2","doi-asserted-by":"publisher","first-page":"2026","DOI":"10.1109\/TPAMI.2011.20","volume":"33","author":"L Zhang","year":"2011","unstructured":"Zhang L, Chen C, Bu J, Cai D, He X, Huang TX (2011) Active learning based on locally linear reconstruction. IEEE Trans Pattern Anal Mach Intell 33(10):2026\u20132038","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1519_CR3","doi-asserted-by":"crossref","unstructured":"O\u2019Neill J, Delany SJ, MacNamee B (2017) Model-free and model-based active learning for regression. Advances in Computational Intelligence Systems, Springer International Publishing 513: 375\u2013386","DOI":"10.1007\/978-3-319-46562-3_24"},{"issue":"5","key":"1519_CR4","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1016\/j.patcog.2014.12.009","volume":"48","author":"H Guo","year":"2015","unstructured":"Guo H, Wang W (2015) An active learning-based SVM multi-class classification model. Pattern Recogn 48(5):1577\u20131597","journal-title":"Pattern Recogn"},{"issue":"6","key":"1519_CR5","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.1016\/j.patcog.2011.12.012","volume":"45","author":"D Tuia","year":"2012","unstructured":"Tuia D, Mu\u00f1oz-Mar\u00ed J, Camps-Valls G (2012) Remote sensing image segmentation by active queries. Pattern Recogn 45(6):2180\u20132192","journal-title":"Pattern Recogn"},{"key":"1519_CR6","doi-asserted-by":"crossref","unstructured":"Seung H, Opper M, Sompolinsky H (1992) Query by committee. In: Proceedings of 5th ACM Annual Workshop on Computational Learning Theory, pp. 287\u2013294","DOI":"10.1145\/130385.130417"},{"issue":"10","key":"1519_CR7","doi-asserted-by":"publisher","first-page":"3751","DOI":"10.1016\/j.patcog.2012.03.022","volume":"45","author":"R Wang","year":"2012","unstructured":"Wang R, Kwong S, Chen D (2012) Inconsistency-based active learning for support vector machines. Pattern Recogn 45(10):3751\u20133767","journal-title":"Pattern Recogn"},{"key":"1519_CR8","unstructured":"Settles B, Craven M, Ray S (2008) Multiple-instance active learning. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 1289\u20131296"},{"key":"1519_CR9","doi-asserted-by":"crossref","unstructured":"Cai W, Zhang Y, Zhou J (2013) Maximizing expected model change for active learning in regression. In: Proceedings of 13th IEEE International Conference Data Mining (ICDM), pp. 51\u201360","DOI":"10.1109\/ICDM.2013.104"},{"issue":"7","key":"1519_CR10","doi-asserted-by":"publisher","first-page":"1668","DOI":"10.1109\/TNNLS.2016.2542184","volume":"28","author":"W Cai","year":"2017","unstructured":"Cai W, Zhang M, Zhang Y (2017) Batch mode active learning for regression with expected model change. IEEE Trans Neural Netw Learn Syst 28(7):1668\u20131681","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"1519_CR11","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1145\/3086820","volume":"36","author":"W Cai","year":"2017","unstructured":"Cai W, Zhang Y, Zhang Y, Zhou S, Wang W, Chen Z, Ding C (2017) Active learning for classification with maximum model change. ACM Trans Inf Syst 36(2):15","journal-title":"ACM Trans Inf Syst"},{"issue":"4","key":"1519_CR12","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1162\/neco.1992.4.4.590","volume":"4","author":"D MacKay","year":"1992","unstructured":"MacKay D (1992) Information-based objective functions for active data selection. Neural Comput 4(4):590\u2013604","journal-title":"Neural Comput"},{"key":"1519_CR13","unstructured":"Cohn D (1994) Neural network exploration using optimal experiment design. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 679\u2013686"},{"key":"1519_CR14","doi-asserted-by":"crossref","unstructured":"Zhang C, Chen T (2003) Annotating retrieval database with active learning. In: Proceedings of 2003 IEEE International Conference on Image Processing, pp. 595","DOI":"10.1109\/ICIP.2003.1246750"},{"key":"1519_CR15","doi-asserted-by":"crossref","unstructured":"Dagli CK, Rajaram S, Huang TS (2006) Utilizing information theoretic diversity for SVM active learn. In: Proceeding of 18th IEEE International Conference on Pattern Recognition, pp. 506\u2013511","DOI":"10.1109\/ICPR.2006.1161"},{"key":"1519_CR16","doi-asserted-by":"crossref","unstructured":"Atkinson A, Donev A, Tobias R (2007) Optimum experimental designs with SAS, Oxford University Press 34","DOI":"10.1093\/oso\/9780199296590.003.0004"},{"key":"1519_CR17","doi-asserted-by":"crossref","unstructured":"Yu K, Bi J, Tresp V (2006) Active learning via transductive experimental design. In: Proceedings of 23rd ACM International Conference on Machine Learning, pp. 1081\u20131088","DOI":"10.1145\/1143844.1143980"},{"key":"1519_CR18","unstructured":"Settles B (2010) Active learning literature survey. University of Wisconsin, Madison 52: 55\u201366"},{"key":"1519_CR19","doi-asserted-by":"crossref","unstructured":"Burbidge R, Rowland JJ, King RD (2007) Active learning for regression based on query by committee. In: Proceedings of International Conference on Intelligent Data Engineering and Automated Learning, pp. 209\u2013218","DOI":"10.1007\/978-3-540-77226-2_22"},{"key":"1519_CR20","unstructured":"Har-Peled S, Roth D, Zimak D (2007) Maximum Margin Coresets for Active and Noise Tolerant Learning. In: Proceeding of International Joint Conferences on Artificial Intelligence Organization, pp. 836\u2013841"},{"key":"1519_CR21","unstructured":"Roy N, McCallum A (2001) Toward optimal active learning through Monte Carlo estimation of error reduction. In: Proceedings of International Conference on Machine Learning, Williamstown, pp. 441\u2013448"},{"key":"1519_CR22","unstructured":"Jingbo Z, Wang H, Yao TB, Tsou B (2008) Active Learning with Sampling by Uncertainty and Density for Word Sense Disambiguation and Text Classification. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 1137\u20131144"},{"key":"1519_CR23","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1613\/jair.295","volume":"4","author":"DA Cohn","year":"1996","unstructured":"Cohn DA, Ghahramani Z, Jordan MI (1996) Active learning with statistical models. J Artif Intell Res 4:129\u2013145","journal-title":"J Artif Intell Res"},{"key":"1519_CR24","unstructured":"Castro R, Willett R, Nowak R (2006) Faster rates in regression via active learning. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 179\u2013186"},{"issue":"1","key":"1519_CR25","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/72.822506","volume":"11","author":"K Fukumizu","year":"2000","unstructured":"Fukumizu K (2000) Statistical active learning in multilayer perceptrons. IEEE Trans Neural Netw Learn Syst 11(1):17\u201326","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1519_CR26","first-page":"141","volume":"7","author":"M Sugiyama","year":"2000","unstructured":"Sugiyama M (2000) Active learning in approximately linear regression based on conditional expectation of generalization error. J Mach Learn Res 7:141\u2013166","journal-title":"J Mach Learn Res"},{"key":"1519_CR27","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1023\/A:1007330508534","volume":"28","author":"Y Freund","year":"1997","unstructured":"Freund Y, Seung HS, Shamir E, Tishby N (1997) Selective sampling using the query by committee algorithm. Mach Learn 28:133\u2013168","journal-title":"Mach Learn"},{"key":"1519_CR28","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.apenergy.2012.09.055","volume":"103","author":"F Douak","year":"2013","unstructured":"Douak F, Melgani F, Benoudjit N (2013) Kernel ridge regression with active learning for wind speed prediction. Appl Energy 103:328\u2013340","journal-title":"Appl Energy"},{"key":"1519_CR29","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.1016\/j.patcog.2014.02.001","volume":"47","author":"B Demir","year":"2014","unstructured":"Demir B, Bruzzone L (2014) A multiple criteria active learning method for support vector regression. Pattern Recogn 47:2558\u20132567","journal-title":"Pattern Recogn"},{"key":"1519_CR30","doi-asserted-by":"crossref","unstructured":"Yu H, Kim S (2010) Passive Sampling for Regression. In: Proceedings of the 10th International Conference on Machine Learning (ICML), pp. 1151\u20131156","DOI":"10.1109\/ICDM.2010.9"},{"key":"1519_CR31","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ins.2018.09.060","volume":"474","author":"D Wu","year":"2019","unstructured":"Wu D, Lin CT, Huang J (2019) Active learning for regression using greedy sampling. Inf Sci 474:90\u2013105","journal-title":"Inf Sci"},{"issue":"11","key":"1519_CR32","first-page":"1","volume":"48","author":"Z Xue","year":"2018","unstructured":"Xue Z, Zhang R, Qin C, Zeng X (2018) A rough \u03bd-twin support vector regression machine. Appl Intell 48(11):1\u201324","journal-title":"Appl Intell"},{"key":"1519_CR33","doi-asserted-by":"crossref","unstructured":"Kriegel HP, Kr\u00f6ger P, Schubert E, Zimek A (2009) LoOP: local outlier probabilities. In: Proceedings of the 18th ACM conference on Information and Knowledge Management, pp. 1649\u20131652","DOI":"10.1145\/1645953.1646195"},{"key":"1519_CR34","unstructured":"Roux NL, Schmidt M, Bach FR (2012) A stochastic gradient method with an exponential convergence rate for finite training sets. In: Proceeding of Advances in Neural Information Processing Systems (NIPS), pp. 2663\u20132671"},{"key":"1519_CR35","unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"1519_CR36","doi-asserted-by":"crossref","unstructured":"Fushiki T (2005) Bootstrap prediction and Bayesian prediction under Misspecified models. Bernoulli:747\u2013758","DOI":"10.3150\/bj\/1126126768"},{"key":"1519_CR37","unstructured":"Zhang Y, Duchi J, Wainwright M (2013) Divide and conquer kernel ridge regression. In: Proceeding of conference on learning theory, pp. 592\u2013617"},{"key":"1519_CR38","unstructured":"Van Vaerenbergh S, Santamar\u0131a I. (2014) Online regression with kernels. Regularization, Optimization, Kernels, and Support Vector Machines 477"},{"key":"1519_CR39","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Herbrich R, Smola AJ (2001) A generalized representer theorem. In: Proceeding of International conference on computational learning theory, pp. 416\u2013426","DOI":"10.1007\/3-540-44581-1_27"},{"key":"1519_CR40","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.enconman.2015.04.078","volume":"100","author":"MG De Giorgi","year":"2015","unstructured":"De Giorgi MG, Congedo PM, Malvoni M, Laforgia D (2015) Error analysis of hybrid photovoltaic power forecasting models: a case study of mediterranean climate. Energy Convers Manag 100:117\u2013130","journal-title":"Energy Convers Manag"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01519-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-019-01519-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01519-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T08:11:40Z","timestamp":1721549500000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-019-01519-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,23]]},"references-count":40,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["1519"],"URL":"https:\/\/doi.org\/10.1007\/s10489-019-01519-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,23]]},"assertion":[{"value":"23 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}