{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:23:29Z","timestamp":1760171009005},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T00:00:00Z","timestamp":1492473600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s10044-017-0620-0","type":"journal-article","created":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T13:48:14Z","timestamp":1492523294000},"page":"1023-1038","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A fast classification strategy for SVM on the large-scale high-dimensional datasets"],"prefix":"10.1007","volume":"21","author":[{"given":"I-Jing","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiunn-Lin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Hung","family":"Yeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,4,18]]},"reference":[{"issue":"4","key":"620_CR1","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1109\/TSMCB.2010.2101593","volume":"41","author":"F Wang","year":"2011","unstructured":"Wang F (2011) Semisupervised metric learning by maximizing constraint margin. IEEE Trans Syst Man Cybern B 41(4):931\u2013939","journal-title":"IEEE Trans Syst Man Cybern B"},{"issue":"12","key":"620_CR2","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1109\/TCYB.2014.2307862","volume":"44","author":"J Yu","year":"2014","unstructured":"Yu J, Rui Y, Tang YY, Tao D (2014) High-order distance based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431\u20132442","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"620_CR3","doi-asserted-by":"crossref","first-page":"3262","DOI":"10.1109\/TIP.2012.2190083","volume":"21","author":"J Yu","year":"2012","unstructured":"Yu J, Tao D (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262\u20133272","journal-title":"IEEE Trans Image Process"},{"key":"620_CR4","doi-asserted-by":"publisher","unstructured":"Li IJ, Wu JL (2014) A new nearest neighbor classification algorithm based on local probability centers. Math Probl Eng 2014. doi: 10.1155\/2014\/324742","DOI":"10.1155\/2014\/324742"},{"key":"620_CR5","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.patcog.2012.08.006","volume":"46","author":"J Yu","year":"2013","unstructured":"Yu J, Tao D, Rui Y, Cheng J (2013) Pairwise constraints based multiview features fusion for scene classification. Pattern Recognit 46:483\u2013496","journal-title":"Pattern Recognit"},{"issue":"3","key":"620_CR6","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TPAMI.2011.142","volume":"34","author":"S Garcia","year":"2012","unstructured":"Garcia S, Derrac J, Cano JR, Herrera F (2012) Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans Pattern Anal Mach Intell 34(3):417\u2013435","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"620_CR7","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/TSMCC.2010.2103939","volume":"42","author":"I Triguero","year":"2012","unstructured":"Triguero I, Derrac J, Garc\u0131a S, Herrera F (2012) A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Trans Syst Man Cybern C 42(1):86\u2013100","journal-title":"IEEE Trans Syst Man Cybern C"},{"key":"620_CR8","unstructured":"Joachims T (1999) Transductive inference for text classification using support vector machines prodigy. In: Proceedings of international conference on machine learning"},{"key":"620_CR9","unstructured":"Zhang H, Berg AC, Maire M, Malik J (2006) SVM-KNN: discriminative nearest neighbor for visual object recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition"},{"issue":"4","key":"620_CR10","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1109\/TPAMI.2012.186","volume":"35","author":"H Nguyen Van","year":"2013","unstructured":"Van Nguyen H, Porikli F (2013) Support vector shape: a classifier-based shape representation. IEEE Trans Pattern Anal Mach Intell 35(4):970\u2013982","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"620_CR11","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s10489-013-0425-9","volume":"39","author":"CW Wang","year":"2013","unstructured":"Wang CW, You WH (2013) Boosting-SVM: effective learning with reduced data dimension. Appl Intell 39(3):465\u2013474","journal-title":"Appl Intell"},{"key":"620_CR12","unstructured":"Chang CC, Lin CJ (2016) LIBSVM: a library for support vector machines. Software Available at: http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm"},{"key":"620_CR13","unstructured":"Rojas SA, Fernandez Reyes D (2005) Adapting multiple kernel parameters for support vector machines using genetic algorithms. In: The 2005 IEEE congress on evolutionary computation, vol 1. pp 626\u2013631"},{"key":"620_CR14","unstructured":"Liang X, Liu F (2002) Choosing multiple parameters for SVM based on genetic algorithm. In: 6th International conference on signal processing, vol 1. pp 117\u2013119"},{"key":"620_CR15","unstructured":"Liu HJ, Wang YN, Lu XF (2005) A method to choose kernel function and its parameters for support vector machines. In: Proceedings of 2005 international conference on machine learning and cybernetics, vol 7. pp 4277\u20134280"},{"key":"620_CR16","unstructured":"Liu S, Jia CY, Ma H (2005) A new weighted support vector machine with GA-based parameter selection. In: Proceedings of 2005 international conference on machine learning and cybernetics, vol 7. pp 4351\u20134355"},{"key":"620_CR17","doi-asserted-by":"crossref","unstructured":"Quang AT, Zhang QL, Li X (2002) Evolving support vector machine parameters. In: Proceedings of 2002 international conference on machine learning and cybernetics, vol 1. pp 548\u2013551","DOI":"10.1109\/ICMLC.2002.1176817"},{"issue":"5","key":"620_CR18","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.patcog.2008.08.030","volume":"42","author":"KP Wu","year":"2009","unstructured":"Wu KP, Wang SD (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognit 42(5):710\u2013717","journal-title":"Pattern Recognit"},{"key":"620_CR19","doi-asserted-by":"crossref","unstructured":"Lee YJ, Mangasarian OL (2001) RSVM: reduced support vector machines. In: Proceedings of 1st SIAM international conference on data mining","DOI":"10.1137\/1.9781611972719.13"},{"key":"620_CR20","doi-asserted-by":"crossref","unstructured":"Yu H, Yang J, Han J (2003) Classifying large data sets using SVMs with hierarchical clusters. In: Proceedings of international conference on knowledge discovery data mining. pp 306\u2013315","DOI":"10.1145\/956750.956786"},{"key":"620_CR21","first-page":"81","volume-title":"Advances in neural information processing systems (NIPS)","author":"GH Bakur","year":"2005","unstructured":"Bakur GH, Bottou L, Weston J (2005) Breaking SVM complexity with cross-training. In: Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems (NIPS), vol 17. MIT Press, Cambridge, pp 81\u201388"},{"issue":"2","key":"620_CR22","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TNN.2009.2039227","volume":"21","author":"F Angiulli","year":"2010","unstructured":"Angiulli F, Astorino A (2010) Scaling up support vector machines using nearest neighbor condensation. IEEE Trans Neural Netw 21(2):351\u2013357","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"620_CR23","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/S0031-3203(00)00184-9","volume":"35","author":"FS Devi","year":"2002","unstructured":"Devi FS, Murty MN (2002) An incremental prototype set building technique. Pattern Recognit 35(2):505\u2013513","journal-title":"Pattern Recognit"},{"key":"620_CR24","volume-title":"Pattern recognition","author":"S Theodoridis","year":"2006","unstructured":"Theodoridis S, Koutroumbas K (2006) Pattern recognition, 3rd edn. Academic Press, London","edition":"3"},{"key":"620_CR25","volume-title":"Pattern classification","author":"PE Hart","year":"2001","unstructured":"Hart PE, Stock DG, Duda RO (2001) Pattern classification, 2nd edn. Wiley, Hoboken","edition":"2"},{"issue":"6","key":"620_CR26","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1162\/neco.1992.4.6.888","volume":"4","author":"L Bottou","year":"1992","unstructured":"Bottou L, Vapnik V (1992) Local learning algorithms. Neural Comput 4(6):888\u2013900","journal-title":"Neural Comput"},{"issue":"5","key":"620_CR27","first-page":"1556","volume":"41","author":"KW Lau","year":"2008","unstructured":"Lau KW, Wu QH (2008) Local prediction of non-linear time series using support vector regression. Pattern Recognit 41(5):1556\u20131564","journal-title":"Pattern Recognit"},{"issue":"3","key":"620_CR28","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1007\/s10489-013-0433-9","volume":"39","author":"IJ Li","year":"2013","unstructured":"Li IJ, Chen JC, Wu JL (2013) A fast prototype reduction method based on template reduction and visualization-induced self-organizing map for nearest neighbor algorithm. Appl Intell 39(3):564\u2013582","journal-title":"Appl Intell"},{"issue":"4","key":"620_CR29","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TKDE.2009.116","volume":"22","author":"HB Cheng","year":"2010","unstructured":"Cheng HB, Tan PN, Jin R (2010) Efficient algorithm for localized support vector machine. IEEE Trans Knowl Data Eng 22(4):537\u2013549","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"620_CR30","volume-title":"Theory of linear and integer programming","author":"A Schrijver","year":"1998","unstructured":"Schrijver A (1998) Theory of linear and integer programming. Wiley, Hoboken"},{"issue":"11","key":"620_CR31","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/TKDE.2007.190645","volume":"19","author":"F Angiulli","year":"2007","unstructured":"Angiulli F (2007) Fast nearest neighbor condensation for large data sets classification. IEEE Trans Knowl Data Eng 19(11):1450\u20131464","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"620_CR32","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/TIT.1968.1054155","volume":"14","author":"PE Hart","year":"1968","unstructured":"Hart PE (1968) The condensed nearest neighbor rule. IEEE Trans Inf Theory 14(3):515\u2013516","journal-title":"IEEE Trans Inf Theory"},{"issue":"3","key":"620_CR33","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/TIT.1972.1054809","volume":"18","author":"W Gates","year":"1972","unstructured":"Gates W (1972) The reduced nearest neighbor rule. IEEE Trans Inf Theory 18(3):431\u2013433","journal-title":"IEEE Trans Inf Theory"},{"key":"620_CR34","unstructured":"Blake C, Keogh E, Merz CJ (2009) UCI repository of machine learning databases. Department of Information and Computer Science, University of California. http:\/\/www.ics.uci.edu\/\u223cmlearn"},{"key":"620_CR35","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1016\/j.patcog.2014.12.016","volume":"48","author":"L Zhang","year":"2015","unstructured":"Zhang L, Zhang Q, Zhang L, Tao D, Huang X, Du B (2015) Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recognit 48:3102\u20133112","journal-title":"Pattern Recognit"},{"key":"620_CR36","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2016.08.006","volume":"62","author":"W Xiong","year":"2016","unstructured":"Xiong W, Zhang L, Du B, Tao D (2016) Combining local and global: rich and robust feature pooling for visual recognition. Pattern Recognit 62:225\u2013235","journal-title":"Pattern Recognit"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10044-017-0620-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-017-0620-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-017-0620-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,21]],"date-time":"2019-09-21T05:48:47Z","timestamp":1569044927000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10044-017-0620-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,18]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["620"],"URL":"https:\/\/doi.org\/10.1007\/s10044-017-0620-0","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,18]]}}}