{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:36:32Z","timestamp":1781109392816,"version":"3.54.1"},"reference-count":10,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,10,1]]},"abstract":"<p>There has become a bottleneck to use support vector machine (SVM) due to the problems such as slow learning speed, large buffer memory requirement, low generalization performance and so on. These problems are caused by large-scale training sample set and outlier data immixed in the other class. Aiming at these problems, this paper proposed a new reduction strategy for large-scale training sample set according to analyzing on the structure of the training sample set based on the point set theory. By using fuzzy clustering method in this new strategy, the potential support vectors are obtained and the non-boundary outlier data immixed in the other class is removed. In view of reducing greatly the scale of the training sample set, it improves the generalization performance of SVM and effectively avoids over-learning. Finally, the experimental results shown the given reduction strategy can not only reduce the train samples of SVM and speed up the train process, but also ensure accuracy of classification.<\/p>","DOI":"10.4018\/japuc.2012100107","type":"journal-article","created":{"date-parts":[[2013,8,9]],"date-time":"2013-08-09T15:24:16Z","timestamp":1376061856000},"page":"63-73","source":"Crossref","is-referenced-by-count":0,"title":["A New SVM Reduction Strategy of Large-Scale Training Sample Sets"],"prefix":"10.4018","volume":"4","author":[{"given":"Fang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Northeastern University, Qinhuangdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfang","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Resource and Material, Northeastern University, Qinhuangdao, China & Tianjin Foreign Studies University, TianJin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6998-7654","authenticated-orcid":true,"given":"Tao","family":"Gao","sequence":"additional","affiliation":[{"name":"North China Electric Power University, Beijing, China & Electronic Information Products Supervision and Inspection Institute of Hebei Province, ShijiaZhuang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"japuc.2012100107-0","doi-asserted-by":"crossref","unstructured":"Agarwal, D. K. (2002). Shrinkage estimator generalizations of proximal support vector machines. In Proceedings of the 8st ACM SIGKDD International Conference of Knowledge Discovery and Data Mining, Edmonton, Canada.","DOI":"10.1145\/775047.775073"},{"key":"japuc.2012100107-1","first-page":"146","article-title":"The edge fuzzy support vector machine (SVM)based on class center thought.","volume":"22","author":"S.Cao","year":"2006","journal-title":"Journal of the Computer Engineering and Applications Communications"},{"key":"japuc.2012100107-2","author":"Q.Cheng","year":"2003","journal-title":"Real variable function and functional analysis foundation"},{"key":"japuc.2012100107-3","author":"N.Cristianini","year":"2006","journal-title":"An introduction to support vector machines and other kernel-based learning methods"},{"key":"japuc.2012100107-4","unstructured":"Daniael, B., & Cao, D. (2004). Training support vector machines using adaptive clustering. In Proceedings of SIAM International Conference on Data Mining, Lake Buena Vista, FL."},{"key":"japuc.2012100107-5","first-page":"1015","article-title":"An improved support vector machine NN \u2013 SVM.","volume":"26","author":"H.Li","year":"2003","journal-title":"Journal of Chinese Journal of Computers Communications"},{"key":"japuc.2012100107-6","author":"Y.Luo","year":"2007","journal-title":"Support vector machine (SVM) in the research on the application of machine learning"},{"key":"japuc.2012100107-7","first-page":"83","article-title":"Support vector machine (SVM) method in text categorization of improvement.","volume":"2","author":"G.Tan","year":"2008","journal-title":"Journal of Information Technology Communication"},{"key":"japuc.2012100107-8","first-page":"4183","article-title":"Machine (SVM) CM - SVM method on improving the speed of training support vector.","volume":"27","author":"X.Xiao","year":"2006","journal-title":"Journal of Computer Engineering and Design Communications"},{"key":"japuc.2012100107-9","author":"Z.Zeng","year":"2007","journal-title":"Support vector machine (SVM) classification machine training and simplified algorithms"}],"container-title":["International Journal of Advanced Pervasive and Ubiquitous Computing"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=79911","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T10:31:19Z","timestamp":1654079479000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/japuc.2012100107"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2012,10,1]]},"references-count":10,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012,10]]}},"URL":"https:\/\/doi.org\/10.4018\/japuc.2012100107","relation":{},"ISSN":["1937-965X","1937-9668"],"issn-type":[{"value":"1937-965X","type":"print"},{"value":"1937-9668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,10,1]]}}}