{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:17:27Z","timestamp":1740107847099,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2015,11,28]],"date-time":"2015-11-28T00:00:00Z","timestamp":1448668800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51006052"],"award-info":[{"award-number":["51006052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2017,5]]},"DOI":"10.1007\/s00500-015-1947-3","type":"journal-article","created":{"date-parts":[[2015,11,28]],"date-time":"2015-11-28T03:02:45Z","timestamp":1448679765000},"page":"2367-2383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A method of combining forward with backward greedy algorithms for sparse approximation to KMSE"],"prefix":"10.1007","volume":"21","author":[{"given":"Yong-Ping","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,11,28]]},"reference":[{"issue":"8","key":"1947_CR1","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1016\/j.patcog.2006.12.015","volume":"40","author":"S An","year":"2007","unstructured":"An S, Liu W, Venkatesh S (2007) Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognit 40(8):2154\u20132162","journal-title":"Pattern Recognit"},{"issue":"4","key":"1947_CR2","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.dss.2009.05.016","volume":"47","author":"P Cortez","year":"2009","unstructured":"Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 47(4):547\u2013553","journal-title":"Decis Support Syst"},{"key":"1947_CR3","volume-title":"Pattern Classification","author":"RO Duda","year":"2001","unstructured":"Duda RO, Hart PE, Stork DG (2001) Pattern Classification. Wiley, UK"},{"issue":"2","key":"1947_CR4","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann. Stat. 32(2):407\u2013451","journal-title":"Ann. Stat."},{"issue":"14","key":"1947_CR5","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1016\/j.ijleo.2014.01.058","volume":"125","author":"H Gan","year":"2014","unstructured":"Gan H (2014) Laplacian regularized kernel minimum squared error and its application to face recognition. Optik 125(14):3524\u20133529","journal-title":"Optik"},{"key":"1947_CR6","doi-asserted-by":"crossref","unstructured":"Jiang J, Chen X, Gan HT (2014) Feature extraction for kernel minimum squared error by sparsity shrinkage, pp 450\u2013453","DOI":"10.4028\/www.scientific.net\/AMM.536-537.450"},{"issue":"3","key":"1947_CR7","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TNN.2006.889500","volume":"18","author":"L Jiao","year":"2007","unstructured":"Jiao L, Bo L, Wang L (2007) Fast sparse approximation for least squares support vector machine. IEEE Trans Neural Netw 18(3):685\u2013697","journal-title":"IEEE Trans Neural Netw"},{"key":"1947_CR8","doi-asserted-by":"publisher","unstructured":"Mika S, Ratsch G, Weston J, Scholkopf B, Muller KR (1999) Fisher discriminant analysis with kernels. In: Proceedings of the 1999 9th IEEE workshop on neural networks for signal processing, pp 41\u201348","DOI":"10.1109\/NNSP.1999.788121"},{"issue":"2","key":"1947_CR9","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/72.914517","volume":"12","author":"KR Muller","year":"2001","unstructured":"Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12(2):181\u2013201","journal-title":"IEEE Trans Neural Netw"},{"issue":"4\u20135","key":"1947_CR10","first-page":"781","volume":"3","author":"PB Nair","year":"2003","unstructured":"Nair PB, Choudhury A, Keane AJ (2003) Some greedy learning algorithms for sparse regression and classification with Mercer kernels. J Mach Learn Res 3(4\u20135):781\u2013801","journal-title":"J Mach Learn Res"},{"issue":"2","key":"1947_CR11","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1137\/S0097539792240406","volume":"24","author":"BK Natarajan","year":"1995","unstructured":"Natarajan BK (1995) Sparse approximate solutions to linear systems. SIAM J Comput 24(2):227\u2013234","journal-title":"SIAM J Comput"},{"issue":"6088","key":"1947_CR12","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"key":"1947_CR13","unstructured":"Saunders C, Gammerman A, Vovk V (1998) Ridge regression learning algorithm in dual variables. In: Proceedings of the fifteenth international conference on machine learning, pp 515\u2013521"},{"issue":"5","key":"1947_CR14","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1109\/72.788641","volume":"10","author":"B Scholkopf","year":"1999","unstructured":"Scholkopf B, Mika S, Burges CJC, Knirsch P, Muller KR, Ratsch G, Smola AJ (1999) Input space versus feature space in kernel-based methods. IEEE Trans Neural Netw 10(5):1000\u20131017","journal-title":"IEEE Trans Neural Netw"},{"key":"1947_CR15","doi-asserted-by":"publisher","unstructured":"Scholkopf B, Herbrich R, Smola AJ (2001) A generalized representer theorem. In: Proceedings of 14th annual conference on computational learning theory, pp 416\u2013426","DOI":"10.1007\/3-540-44581-1_27"},{"key":"1947_CR16","volume-title":"Learning with Kernels","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf B, Smola AJ (2002) Learning with Kernels. MIT Press, Cambridge"},{"key":"1947_CR17","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neucom.2013.07.035","volume":"124","author":"J Shim","year":"2014","unstructured":"Shim J, Bin O, Hwang C (2014) Semiparametric spatial effects kernel minimum squared error model for predicting housing sales prices. Neurocomputing 124:81\u201388","journal-title":"Neurocomputing"},{"issue":"3","key":"1947_CR18","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JAK Suykens","year":"1999","unstructured":"Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293\u2013300","journal-title":"Neural Process Lett"},{"key":"1947_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer, New York"},{"issue":"5","key":"1947_CR20","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"VN Vapnik","year":"1999","unstructured":"Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988\u2013999","journal-title":"IEEE Trans Neural Netw"},{"issue":"1\u20133","key":"1947_CR21","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1023\/A:1013955821559","volume":"48","author":"P Vincent","year":"2002","unstructured":"Vincent P, Bengio Y (2002) Kernel matching pursuit. Mach Learn 48(1\u20133):165\u2013187","journal-title":"Mach Learn"},{"issue":"1","key":"1947_CR22","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s00521-012-0813-9","volume":"23","author":"JH Wang","year":"2013","unstructured":"Wang JH, Wang P, Li Q, You J (2013) Improvement of the kernel minimum squared error model for fast feature extraction. Neural Comput. Appl. 23(1):53\u201359","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"1947_CR23","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1016\/j.patcog.2005.10.029","volume":"39","author":"Y Xu","year":"2006","unstructured":"Xu Y, Zhang D, Jin Z, Li M, Yang J-Y (2006) A fast kernel-based nonlinear discriminant analysis for multi-class problems. Pattern Recognit 39(6):1026\u20131033","journal-title":"Pattern Recognit"},{"key":"1947_CR24","unstructured":"Xu Y, Yang JY, Lu JF (2005) An efficient kernel-based nonlinear regression method for two-class classification. In: Proceedings of 2005 international conference on machine learning and cybernetics, pp 4442\u20134445"},{"key":"1947_CR25","unstructured":"Xu J, Zhang X, Li Y (2001) Kernel MSE algorithm: a unified framework for KFD, LS-SVM and KRR. In: Proceedings of the international joint conference on neural networks, pp 1486\u20131491"},{"key":"1947_CR26","volume-title":"Matrix analysis and applications","author":"X Zhang","year":"2004","unstructured":"Zhang X (2004) Matrix analysis and applications. Tsinghua University Press, Beijing"},{"issue":"17","key":"1947_CR27","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1016\/j.neucom.2011.04.004","volume":"74","author":"Y-P Zhao","year":"2011","unstructured":"Zhao Y-P, Sun J-G, Du Z-H, Zhang Z-A, Zhang H-B (2011) Pruning least objective contribution in KMSE. Neurocomputing 74(17):3009\u20133018","journal-title":"Neurocomputing"},{"issue":"10","key":"1947_CR28","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1016\/j.neucom.2011.01.020","volume":"74","author":"Y-P Zhao","year":"2011","unstructured":"Zhao Y-P, Du Z-H, Zhang Z-A, Zhang H-B (2011) A fast method of feature extraction for kernel MSE. Neurocomputing 74(10):1654\u20131663","journal-title":"Neurocomputing"},{"key":"1947_CR29","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.ins.2014.02.117","volume":"270","author":"Y-P Zhao","year":"2014","unstructured":"Zhao Y-P, Wang K-K, Liu J, Huerta R (2014) Incremental kernel minimum squared error (KMSE). Inf Sci 270:92\u2013111","journal-title":"Inf Sci"},{"key":"1947_CR30","doi-asserted-by":"publisher","unstructured":"Zhu Q (2009) A method for rapid feature extraction based on KMSE. In: Proceedings of the 2009 WRI Global Congress on Intelligent Systems, pp 335\u2013338","DOI":"10.1109\/GCIS.2009.160"},{"issue":"16\u201318","key":"1947_CR31","doi-asserted-by":"publisher","first-page":"3334","DOI":"10.1016\/j.neucom.2010.04.007","volume":"73","author":"Q Zhu","year":"2010","unstructured":"Zhu Q (2010) Reformative nonlinear feature extraction using kernel MSE. Neurocomputing 73(16\u201318):3334\u20133337","journal-title":"Neurocomputing"},{"key":"1947_CR32","doi-asserted-by":"publisher","unstructured":"Zhu Q, Xu Y, Cui J, Chen C, Wang J, Wu X, Zhao Y (2009) A method for constructing simplified kernel model based on Kernel-MSE. In: Proceedings of Pacific conference on computational intelligence and industrial applications, pp 237\u2013240","DOI":"10.1109\/PACIIA.2009.5406447"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-015-1947-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-015-1947-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-015-1947-3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-015-1947-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T21:16:50Z","timestamp":1567372610000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-015-1947-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,28]]},"references-count":32,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2017,5]]}},"alternative-id":["1947"],"URL":"https:\/\/doi.org\/10.1007\/s00500-015-1947-3","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2015,11,28]]}}}