{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:23:17Z","timestamp":1758273797922},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,4,27]],"date-time":"2018-04-27T00:00:00Z","timestamp":1524787200000},"content-version":"tdm","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":["61572240"],"award-info":[{"award-number":["61572240"]}],"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":["61601202"],"award-info":[{"award-number":["61601202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20140571"],"award-info":[{"award-number":["BK20140571"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Project Program of the National Laboratory of Pattern Recognition","award":["201600005"],"award-info":[{"award-number":["201600005"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1007\/s11280-018-0576-z","type":"journal-article","created":{"date-parts":[[2018,4,27]],"date-time":"2018-04-27T09:32:51Z","timestamp":1524821571000},"page":"717-734","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Co-regularized kernel ensemble regression"],"prefix":"10.1007","volume":"22","author":[{"given":"Dickson Keddy","family":"Wornyo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang-Jun","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu-Cheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,4,27]]},"reference":[{"issue":"10","key":"576_CR1","first-page":"203","volume":"11","author":"D Basak","year":"2007","unstructured":"Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inform. Process.-Lett. Rev. 11(10), 203\u2013224 (2007)","journal-title":"Neural Inform. Process.-Lett. Rev."},{"issue":"3","key":"576_CR2","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/TPWRS.2008.922526","volume":"23","author":"H Bludszuweit","year":"2008","unstructured":"Bludszuweit, H., Dom\u00ednguez-Navarro, J.A., Llombart, A.: Statistical analysis of wind power forecast error. IEEE Trans. Power Syst. 23(3), 983\u2013991 (2008)","journal-title":"IEEE Trans. Power Syst."},{"issue":"1","key":"576_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"576_CR4","unstructured":"Chen, P., Tao, S., Xiao, X., Li, L.: Uncertainty level of voltage in distribution network: an analysis model with elastic net and application in storage configuration. IEEE Transactions on Smart Grid (2016)"},{"key":"576_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, C.-K., Graham, R., Kang, I., Park, D., Wang, X.: Tree structures and algorithms for physical design. In: Proc. ISPD (2018)","DOI":"10.1145\/3177540.3177564"},{"key":"576_CR6","unstructured":"Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp 155\u2013161 (1997)"},{"issue":"3","key":"576_CR7","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.ijforecast.2015.11.017","volume":"32","author":"P Exterkate","year":"2016","unstructured":"Exterkate, P., Groenen, P.J., Heij, C., van Dijk, D.: Nonlinear forecasting with many predictors using kernel ridge regression. Int. J. Forecast. 32(3), 736\u2013753 (2016)","journal-title":"Int. J. Forecast."},{"key":"576_CR8","doi-asserted-by":"crossref","unstructured":"Fan, C., Rey, S.J., Myint, S.W.: Spatially filtered ridge regression (sfrr): A regression framework to understanding impacts of land cover patterns on urban climate. Transactions in GIS (2016)","DOI":"10.1111\/tgis.12240"},{"key":"576_CR9","unstructured":"Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: Icml, vol. 96, pp. 148\u2013156. Bari (1996)"},{"issue":"3","key":"576_CR10","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00530-015-0494-1","volume":"23","author":"L Gao","year":"2017","unstructured":"Gao, L., Song, J., Liu, X., Shao, J., Liu, J., Shao, J.: Learning in high-dimensional multimedia data: the state of the art. Multimed Syst 23(3), 303\u2013313 (2017)","journal-title":"Multimed Syst"},{"key":"576_CR11","doi-asserted-by":"crossref","unstructured":"Gao, L., Song, J., Nie, F., Yan, Y., Sebe, N., Tao Shen, H.: Optimal graph learning with partial tags and multiple features for image and video annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4371\u20134379 (2015)","DOI":"10.1109\/CVPR.2015.7299066"},{"key":"576_CR12","doi-asserted-by":"crossref","unstructured":"Gao, W., Peng, Y.: Ideal kernel-based multiple kernel learning for spectral-spatial classification of hyperspectral image. In: IEEE Geoscience and Remote Sensing Letters (2017)","DOI":"10.1109\/LGRS.2017.2695534"},{"key":"576_CR13","unstructured":"Han, Y., Yang, Y., Zhou, X.: Co-regularized ensemble for feature selection. In: IJCAI, vol. 13, pp 1380\u20131386 (2013)"},{"issue":"1","key":"576_CR14","first-page":"45","volume":"6","author":"MAM Hasan","year":"2014","unstructured":"Hasan, M.A.M., Nasser, M., Pal, B., Ahmad, S.: Support vector machine and random forest modeling for intrusion detection system (ids). J. Intell. Learn. Syst. Appl. 6(1), 45 (2014)","journal-title":"J. Intell. Learn. Syst. Appl."},{"issue":"4","key":"576_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Their. Appl. 13(4), 18\u201328 (1998)","journal-title":"IEEE Intell. Syst. Their. Appl."},{"key":"576_CR16","doi-asserted-by":"crossref","unstructured":"Heinermann, J., Kramer, O.: Precise wind power prediction with svm ensemble regression. In: International Conference on Artificial Neural Networks, pp. 797\u2013804. Springer (2014)","DOI":"10.1007\/978-3-319-11179-7_100"},{"key":"576_CR17","unstructured":"Homrighausen, D., McDonald, D.J.: Risk estimation for high-dimensional lasso regression (2016). arXiv: 1602.01522"},{"key":"576_CR18","unstructured":"Ji, S., Sun, L., Jin, R., Ye, J.: Multi-label multiple kernel learning. In: Advances in Neural Information Processing Systems, pp 777\u2013784 (2009)"},{"key":"576_CR19","doi-asserted-by":"crossref","unstructured":"Johnson, J., Ballan, L., Fei-Fei, L.: Love thy neighbors: image annotation by exploiting image metadata. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4624\u20134632 (2015)","DOI":"10.1109\/ICCV.2015.525"},{"issue":"7553","key":"576_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"3","key":"576_CR21","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1289\/ehp.1408933","volume":"124","author":"V Lenters","year":"2016","unstructured":"Lenters, V., Portengen, L., Rignell-Hydbom, A., J\u00f6nsson, B. A., Lindh, C.H., Piersma, A.H., Toft, G., Bonde, J.P., Heederik, D., Rylander, L., et al.: Prenatal phthalate, perfluoroalkyl acid, and organochlorine exposures and term birth weight in three birth cohorts: multi-pollutant models based on elastic net regression. Environ. Health Perspect. 124(3), 365 (2016)","journal-title":"Environ. Health Perspect."},{"issue":"3-4","key":"576_CR22","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00521-011-0771-7","volume":"22","author":"G Li","year":"2013","unstructured":"Li, G., Niu, P.: An enhanced extreme learning machine based on ridge regression for regression. Neural Comput. Appl. 22(3-4), 803\u2013810 (2013)","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"576_CR23","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TPAMI.2010.183","volume":"33","author":"Y-Y Lin","year":"2011","unstructured":"Lin, Y.-Y., Liu, T.-L., Fuh, C.-S.: Multiple kernel learning for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1147\u20131160 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"576_CR24","unstructured":"Liu, F., Xiang, T., Hospedales, T.M., Yang, W., Sun, C.: Semantic regularisation for recurrent image annotation (2016). arXiv: 1611.05490"},{"key":"576_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.06.096","volume":"172","author":"W Liu","year":"2016","unstructured":"Liu, W., Liu, H., Tao, D., Wang, Y., Lu, K.: Manifold regularized kernel logistic regression for Web image annotation. Neurocomputing 172, 3\u20138 (2016)","journal-title":"Neurocomputing"},{"key":"576_CR26","doi-asserted-by":"crossref","unstructured":"Lu, J., Wang, G., Moulin, P.: Image set classification using holistic multiple order statistics features and localized multi-kernel metric learning. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 329\u2013336, IEEE (2013)","DOI":"10.1109\/ICCV.2013.48"},{"issue":"17","key":"576_CR27","doi-asserted-by":"publisher","first-page":"2406","DOI":"10.1093\/bioinformatics\/btr410","volume":"27","author":"Y Lu","year":"2011","unstructured":"Lu, Y., Zhou, Y., Qu, W., Deng, M., Zhang, C.: A lasso regression model for the construction of microrna-target regulatory networks. Bioinformatics 27 (17), 2406\u20132413 (2011)","journal-title":"Bioinformatics"},{"issue":"5","key":"576_CR28","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1016\/j.patcog.2014.10.017","volume":"48","author":"S Mao","year":"2015","unstructured":"Mao, S., Jiao, L., Xiong, L., Gou, S., Chen, B., Yeung, S.-K.: Weighted classifier ensemble based on quadratic form. Pattern Recogn. 48(5), 1688\u20131706 (2015)","journal-title":"Pattern Recogn."},{"key":"576_CR29","volume-title":"Introduction to Linear Regression Analysis","author":"DC Montgomery","year":"2015","unstructured":"Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis. Wiley, Hoboken (2015)"},{"key":"576_CR30","doi-asserted-by":"crossref","unstructured":"Morvant, E., Habrard, A., Ayache, S.: Majority vote of diverse classifiers for late fusion. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp. 153\u2013162. Springer (2014)","DOI":"10.1007\/978-3-662-44415-3_16"},{"issue":"2","key":"576_CR31","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/72.914517","volume":"12","author":"K-R Muller","year":"2001","unstructured":"Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181\u2013201 (2001)","journal-title":"IEEE Trans. Neural Netw."},{"key":"576_CR32","volume-title":"Machine Learning: A Probabilistic Perspective","author":"KP Murphy","year":"2012","unstructured":"Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge (2012)"},{"key":"576_CR33","doi-asserted-by":"crossref","unstructured":"Niazmardi, S., Safari, A., Homayouni, S.: A novel multiple kernel learning framework for multiple feature classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2017)","DOI":"10.1109\/JSTARS.2017.2697417"},{"issue":"3","key":"576_CR34","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TKDE.2014.2339860","volume":"27","author":"B Qian","year":"2015","unstructured":"Qian, B., Wang, X., Ye, J., Davidson, I.: A reconstruction error based framework for multi-label and multi-view learning. IEEE Trans. Knowl. Data Eng. 27(3), 594\u2013607 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"576_CR35","unstructured":"Qiu, S., Lane, T.: Multiple Kernel Learning for Support Vector Regression, Computer Science Department, The University of New Mexico, Albuquerque, NM, USA, Tech. Rep, sp 1 (2005)"},{"key":"576_CR36","unstructured":"Quinlan, J.R., et al.: Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence, vol. 92, pp. 343\u2013348, Singapore (1992)"},{"issue":"1","key":"576_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2853073.2853083","volume":"41","author":"SS Rathore","year":"2016","unstructured":"Rathore, S.S., Kumar, S.: A decision tree regression based approach for the number of software faults prediction. ACM SIGSOFT Software Engineering Notes 41(1), 1\u20136 (2016)","journal-title":"ACM SIGSOFT Software Engineering Notes"},{"issue":"1","key":"576_CR38","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s10614-013-9411-x","volume":"45","author":"G Santamar\u00eda-Bonfil","year":"2015","unstructured":"Santamar\u00eda-Bonfil, G., Frausto-Sol\u00eds, J., V\u00e1zquez-Rodarte, I.: Volatility forecasting using support vector regression and a hybrid genetic algorithm. Comput. Econ. 45(1), 111\u2013133 (2015)","journal-title":"Comput. Econ."},{"key":"576_CR39","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"issue":"4","key":"576_CR40","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1109\/TPAMI.2015.2441053","volume":"38","author":"U Schmidt","year":"2016","unstructured":"Schmidt, U., Jancsary, J., Nowozin, S., Roth, S., Rother, C.: Cascades of regression tree fields for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 677\u2013689 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"576_CR41","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)"},{"key":"576_CR42","unstructured":"Shah, S.A.A., Nadeem, U., Bennamoun, M., Sohel, F., Togneri, R.: Efficient image set classification using linear regression based image reconstruction (2017). arXiv: 1701.02485"},{"key":"576_CR43","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel Methods for Pattern Analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)"},{"issue":"7580","key":"576_CR44","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/nature15736","volume":"528","author":"N Stransky","year":"2015","unstructured":"Stransky, N., Ghandi, M., Kryukov, G.V., Garraway, L.A., Leh\u00e1r, J., Liu, M., Sonkin, D., Kauffmann, A., Venkatesan, K., Edelman, E.J., et al.: Pharmacogenomic agreement between two cancer cell line data sets. Nature 528 (7580), 84 (2015)","journal-title":"Nature"},{"issue":"6","key":"576_CR45","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and qsar modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947\u20131958 (2003)","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"1-2","key":"576_CR46","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10994-009-5150-6","volume":"79","author":"M Szafranski","year":"2010","unstructured":"Szafranski, M., Grandvalet, Y., Rakotomamonjy, A.: Composite kernel learning. Mach. Learn. 79(1-2), 73\u2013103 (2010)","journal-title":"Mach. Learn."},{"key":"576_CR47","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.neucom.2017.05.036","volume":"266","author":"J Tang","year":"2017","unstructured":"Tang, J., Tian, Y.: A multi-kernel framework with nonparallel support vector machine. Neurocomputing 266, 226\u2013238 (2017)","journal-title":"Neurocomputing"},{"key":"576_CR48","volume-title":"Statistical Learning Theory, vol. 1","author":"VN Vapnik","year":"1998","unstructured":"Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)"},{"key":"576_CR49","doi-asserted-by":"crossref","unstructured":"Wahba, G.: Spline Models for Observational Data. SIAM (1990)","DOI":"10.1137\/1.9781611970128"},{"key":"576_CR50","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.renene.2014.11.011","volume":"76","author":"J Wang","year":"2015","unstructured":"Wang, J., Qin, S., Zhou, Q., Jiang, H.: Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China. Renew. Energy 76, 91\u2013101 (2015)","journal-title":"Renew. Energy"},{"key":"576_CR51","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: Cnn-rnn: a unified framework for multi-label image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2285\u20132294 (2016)","DOI":"10.1109\/CVPR.2016.251"},{"key":"576_CR52","doi-asserted-by":"crossref","unstructured":"Wang, X., Gao, L., Wang, P., Sun, X., Liu, X.: Two-stream 3d convnet fusion for action recognition in videos with arbitrary size and length. IEEE Transactions on Multimedia (2017)","DOI":"10.1109\/TMM.2017.2749159"},{"key":"576_CR53","doi-asserted-by":"crossref","unstructured":"Wang, Y., Feng, D., Li, D., Chen, X., Zhao, Y., Niu, X.: A mobile recommendation system based on logistic regression and gradient boosting decision trees. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1896\u20131902, IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727431"},{"key":"576_CR54","doi-asserted-by":"crossref","unstructured":"Wu, H., Cai, Y., Wu, Y., Zhong, R., Li, Q., Zheng, J., Lin, D., Li, Y.: Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. BioScience Trends, pp. 2017\u201301 035 (2017)","DOI":"10.5582\/bst.2017.01035"},{"key":"576_CR55","doi-asserted-by":"crossref","unstructured":"Yu, M., Xie, Z., Shi, H., Hu, Q.: Locally weighted ensemble learning for regression. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 65\u201376. Springer (2016)","DOI":"10.1007\/978-3-319-31753-3_6"},{"issue":"2","key":"576_CR56","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/TIP.2016.2627806","volume":"26","author":"C Zhang","year":"2017","unstructured":"Zhang, C., Fu, H., Hu, Q., Zhu, P., Cao, X.: Flexible multi-view dimensionality co-reduction. IEEE Trans. Image Process. 26(2), 648\u2013659 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"576_CR57","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","volume":"54","author":"F Zhang","year":"2016","unstructured":"Zhang, F., Du, B., Zhang, L.: Scene classification via a gradient boosting random convolutional network framework. IEEE Trans. Geosci. Remote Sens. 54(3), 1793\u20131802 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"576_CR58","first-page":"015","volume":"3","author":"S Zhang","year":"2012","unstructured":"Zhang, S., Dong, X., Guan, Y.: Synonym recognition based on user behaviors in e-commerce [j]. J. Chin. Inform. Process. 3, 015 (2012)","journal-title":"J. Chin. Inform. Process."},{"issue":"3","key":"576_CR59","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1148\/radiol.2273011499","volume":"227","author":"KH Zou","year":"2003","unstructured":"Zou, K.H., Tuncali, K., Silverman, S.G.: Correlation and simple linear regression. Radiology 227(3), 617\u2013628 (2003)","journal-title":"Radiology"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-018-0576-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11280-018-0576-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-018-0576-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T04:40:54Z","timestamp":1661056854000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11280-018-0576-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,27]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,3]]}},"alternative-id":["576"],"URL":"https:\/\/doi.org\/10.1007\/s11280-018-0576-z","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,27]]},"assertion":[{"value":"16 August 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}