{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:32:18Z","timestamp":1758270738946,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:00:00Z","timestamp":1641600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:00:00Z","timestamp":1641600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003593","name":"conselho nacional de desenvolvimento cientco e tecnol\u00f3gico","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"name":"advanced institute for artificial intelligence"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00521-021-06615-1","type":"journal-article","created":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:03:13Z","timestamp":1641600193000},"page":"18187-18199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Effective sample size, dimensionality, and generalization in covariate shift adaptation"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4950-2795","authenticated-orcid":false,"given":"Felipe","family":"Maia Polo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-9895","authenticated-orcid":false,"given":"Renato","family":"Vicente","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"issue":"3","key":"6615_CR1","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.engappai.2012.05.023","volume":"26","author":"CL Wu","year":"2013","unstructured":"Wu CL, Chau K-W (2013) Prediction of rainfall time series using modular soft computingmethods. Eng Appl Artif Intell 26(3):997\u20131007","journal-title":"Eng Appl Artif Intell"},{"key":"6615_CR2","doi-asserted-by":"publisher","first-page":"102053","DOI":"10.1016\/j.aquaeng.2020.102053","volume":"89","author":"B Ashkan","year":"2020","unstructured":"Ashkan B, Amin N, Amin T-G (2020) Deep learning-based appearance features extraction for automated carp species identification. Aquacult Eng 89:102053","journal-title":"Aquacult Eng"},{"key":"6615_CR3","doi-asserted-by":"publisher","first-page":"157346","DOI":"10.1109\/ACCESS.2020.3019574","volume":"8","author":"X Cheng","year":"2020","unstructured":"Cheng X, Feng Z-K, Niu W-J (2020) Forecasting monthly runoff time series by single-layer feedforward artificial neural network and grey wolf optimizer. IEEE Access 8:157346\u2013157355","journal-title":"IEEE Access"},{"issue":"1","key":"6615_CR4","first-page":"892","volume":"13","author":"M Ghalandari","year":"2019","unstructured":"Ghalandari M, Ziamolki A, Mosavi A, Shamshirband S, Chau K-W, Bornassi S (2019) Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments. Eng Appl Comput Fluid Mech 13(1):892\u2013904","journal-title":"Eng Appl Comput Fluid Mech"},{"key":"6615_CR5","doi-asserted-by":"publisher","first-page":"25111","DOI":"10.1109\/ACCESS.2020.2970836","volume":"8","author":"Y Fan","year":"2020","unstructured":"Fan Y, Kangkang X, Hui W, Zheng Y, Tao B (2020) Spatiotemporal modeling for nonlinear distributed thermal processes based on kl decomposition, mlp and lstm network. IEEE Access 8:25111\u201325121","journal-title":"IEEE Access"},{"key":"6615_CR6","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.engappai.2015.07.019","volume":"45","author":"R Taormina","year":"2015","unstructured":"Taormina R, Chau K-W (2015) Ann-based interval forecasting of streamflow discharges using the lube method and mofips. Eng Appl Artif Intell 45:429\u2013440","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"6615_CR7","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/S0378-3758(00)00115-4","volume":"90","author":"H Shimodaira","year":"2000","unstructured":"Shimodaira H (2000) Improving predictive inference under covariate shift by weighting the log-likelihood function. J Stat Plann Inference 90(2):227\u2013244","journal-title":"J Stat Plann Inference"},{"key":"6615_CR8","volume-title":"Machine learning in non-stationary environments: introduction to covariate shift adaptation","author":"S Masashi","year":"2012","unstructured":"Masashi S, Motoaki K (2012) Machine learning in non-stationary environments: introduction to covariate shift adaptation. MIT Press, Cambridge"},{"key":"6615_CR9","doi-asserted-by":"crossref","unstructured":"Jiayuan H, Arthur G, Karsten B, Bernhard S, Alex JS (2007) Correcting sample selection bias by unlabeled data. In: Advances in neural information processing systems, pp 601\u2013608","DOI":"10.7551\/mitpress\/7503.003.0080"},{"issue":"May","key":"6615_CR10","first-page":"985","volume":"8","author":"S Masashi","year":"2007","unstructured":"Masashi S, Matthias K, Klaus-Robert M (2007) Covariate shift adaptation by importance weighted cross validation. J Mach Learn Res 8(May):985\u20131005","journal-title":"J Mach Learn Res"},{"issue":"Jul","key":"6615_CR11","first-page":"1391","volume":"10","author":"K Takafumi","year":"2009","unstructured":"Takafumi K, Shohei H, Masashi S (2009) A least-squares approach to direct importance estimation. J Mach Learn Res 10(Jul):1391\u20131445","journal-title":"J Mach Learn Res"},{"key":"6615_CR12","unstructured":"Fulton W, Cynthia R (2017) Extreme dimension reduction for handling covariate shift. arXiv:1711.10938"},{"issue":"4","key":"6615_CR13","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s10463-008-0197-x","volume":"60","author":"M Sugiyama","year":"2008","unstructured":"Sugiyama M, Suzuki T, Nakajima S, Kashima H, von B\u00fcnau P, Kawanabe M (2008) Direct importance estimation for covariate shift adaptation. Ann Inst Stat Math 60(4):699\u2013746","journal-title":"Ann Inst Stat Math"},{"key":"6615_CR14","unstructured":"Rafael I, Ann L, Chad S (2014) High-dimensional density ratio estimation with extensions to approximate likelihood computation. In: Artificial intelligence and statistics, pp 420\u2013429"},{"key":"6615_CR15","unstructured":"Song L, Akiko T, Taiji S, Kenji F (2017) Trimmed density ratio estimation. In: Advances in neural information processing systems, pp 4518\u20134528"},{"key":"6615_CR16","unstructured":"Sashank JR, Barnabas P, Alex S (2015) Doubly robust covariate shift correction. In: Twenty-Ninth AAAI conference on artificial intelligence"},{"issue":"4","key":"6615_CR17","first-page":"5","volume":"3","author":"G Arthur","year":"2009","unstructured":"Arthur G, Alex S, Jiayuan H, Marcel S, Karsten B, Bernhard S (2009) Covariate shift by kernel mean matching. Dataset Shift Mach Learn 3(4):5","journal-title":"Dataset Shift Mach Learn"},{"key":"6615_CR18","first-page":"3449","volume":"89","author":"S Petar","year":"2019","unstructured":"Petar S, Mingming G, Jaime GC, Kun Z (2019) Low-dimensional density ratio estimation for covariate shift correction. Proc Mach Learn Res 89:3449","journal-title":"Proc Mach Learn Res"},{"key":"6615_CR19","unstructured":"Christian PR, George C, George C (2010) Introducing Monte Carlo methods with r, volume\u00a018. Springer"},{"key":"6615_CR20","unstructured":"Art BO (2013) Monte Carlo theory, methods and examples."},{"key":"6615_CR21","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.sigpro.2016.08.025","volume":"131","author":"L Martino","year":"2017","unstructured":"Martino L, Elvira V, Louzada F (2017) Effective sample size for importance sampling based on discrepancy measures. Signal Process 131:386\u2013401","journal-title":"Signal Process"},{"key":"6615_CR22","unstructured":"Cortes C, Mansour Y, Mohri M (2010) Learning bounds for importance weighting. In: Advances in neural information processing systems, pp 442\u2013450"},{"issue":"1","key":"6615_CR23","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10472-018-9613-y","volume":"85","author":"C Cortes","year":"2019","unstructured":"Cortes C, Greenberg S, Mohri M (2019) Relative deviation learning bounds and generalization with unbounded loss functions. Ann Math Artif Intell 85(1):45\u201370","journal-title":"Ann Math Artif Intell"},{"key":"6615_CR24","unstructured":"V\u00edctor E, Luca M, Christian PR (2018) Rethinking the effective sample size. arXiv:1809.04129"},{"issue":"3","key":"6615_CR25","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1214\/17-STS611","volume":"32","author":"S Agapiou","year":"2017","unstructured":"Agapiou S, Omiros P, Sanz-Alonso D, Stuart AM et al (2017) Importance sampling: intrinsic dimension and computational cost. Stat Sci 32(3):405\u2013431","journal-title":"Stat Sci"},{"issue":"7","key":"6615_CR26","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.1109\/TIT.2014.2320500","volume":"60","author":"T Van Erven","year":"2014","unstructured":"Van Erven T, Harremos P (2014) R\u00e9nyi divergence and Kullback-Leibler divergence. IEEE Trans Inf Theory 60(7):3797\u20133820","journal-title":"IEEE Trans Inf Theory"},{"key":"6615_CR27","volume-title":"A theory of learning and generalization","author":"V Mathukumalli","year":"2002","unstructured":"Mathukumalli V (2002) A theory of learning and generalization. Springer, Berlin"},{"key":"6615_CR28","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"H Trevor","year":"2009","unstructured":"Trevor H, Robert T, Jerome F (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin"},{"key":"6615_CR29","unstructured":"Taiji S, Masashi S (2010) Sufficient dimension reduction via squared-loss mutual information estimation. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 804\u2013811"},{"key":"6615_CR30","doi-asserted-by":"crossref","unstructured":"Tian L, Deniz E, Umut O, Yonghong H (2006) Estimating mutual information using gaussian mixture model for feature ranking and selection. In: The 2006 IEEE international joint conference on neural network proceedings, pp 5034\u20135039. IEEE","DOI":"10.1109\/IJCNN.2006.247209"},{"key":"6615_CR31","doi-asserted-by":"crossref","unstructured":"Emil E, Amaury L, Juha K (2014) Variable selection for regression problems using gaussian mixture models to estimate mutual information. In: 2014 international joint conference on neural networks (IJCNN), pp 1606\u20131613. IEEE","DOI":"10.1109\/IJCNN.2014.6889561"},{"key":"6615_CR32","unstructured":"Isabelle G, Andr\u00e9 E (2003)An introduction to variable and feature selection. J Mach Learn Res 3 (Mar):1157\u20131182"},{"key":"6615_CR33","volume-title":"Density ratio estimation in machine learning","author":"S Masashi","year":"2012","unstructured":"Masashi S, Taiji S, Takafumi K (2012) Density ratio estimation in machine learning. Cambridge University Press, Cambridge"},{"key":"6615_CR34","volume-title":"A course in mathematical statistics","author":"GR George","year":"1997","unstructured":"George GR (1997) A course in mathematical statistics. Elsevier, Amsterdam"},{"issue":"7","key":"6615_CR35","doi-asserted-by":"publisher","first-page":"3884","DOI":"10.1109\/TSP.2010.2047340","volume":"58","author":"Yu Qiao","year":"2010","unstructured":"Qiao Yu, Minematsu N (2010) A study on invariance of $$f$$-divergence and its application to speech recognition. IEEE Trans Signal Process 58(7):3884\u20133890","journal-title":"IEEE Trans Signal Process"},{"issue":"11","key":"6615_CR36","doi-asserted-by":"publisher","first-page":"5973","DOI":"10.1109\/TIT.2016.2603151","volume":"62","author":"I Sason","year":"2016","unstructured":"Sason I, Verd\u00fa S (2016) f-divergence inequalities. IEEE Trans Inf Theory 62(11):5973\u20136006","journal-title":"IEEE Trans InfTheory"},{"key":"6615_CR37","first-page":"2825","volume":"12","author":"P Fabian","year":"2011","unstructured":"Fabian P, Ga\u00ebl V, Alexandre G, Vincent M, Bertrand T, Olivier G, Mathieu B, Peter P, Ron W, Vincent D et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06615-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06615-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06615-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T22:08:21Z","timestamp":1726438101000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06615-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,8]]},"references-count":37,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["6615"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06615-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,1,8]]},"assertion":[{"value":"3 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}