{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:35:00Z","timestamp":1761676500802,"version":"3.41.0"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2017,5,9]],"date-time":"2017-05-09T00:00:00Z","timestamp":1494288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>Scaling up kernel support vector machine (SVM) training has been an important topic in recent years. Despite its theoretical elegance, training kernel SVM is impractical when facing millions of data. The divide-and-conquer (DC) strategy is a natural framework of handling gigantic problems, and the divide-and-conquer solver for kernel SVM (DC-SVM) is able to train kernel SVM with millions of data with limited time cost. However, there are some drawbacks of the DC-SVM approach. First, it used an unsupervised clustering method to partition the whole problem, which is prone to construct singular subsets, and, second, it is hard to balance the computation load between sub-problems. To address these issues, this article proposed a load-balancing partition method for kernel SVM. First, it clusters sample from one class and then assigns data samples to the cluster centers by a distance measure and construct sub-problems; in this way, it is able to control the computation load and avoid singular problems. Experimental results show that the proposed method has better load-balancing performance than DC-SVM, which implies that it is suitable for distributed and embedding systems.<\/jats:p>","DOI":"10.1145\/3005347","type":"journal-article","created":{"date-parts":[[2017,5,10]],"date-time":"2017-05-10T18:08:53Z","timestamp":1494439733000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Load-Balancing Divide-and-Conquer SVM Solver"],"prefix":"10.1145","volume":"16","author":[{"given":"Xiaogang","family":"Chen","sequence":"first","affiliation":[{"name":"Tsinghua Univeristy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Z. Jane","family":"Wang","sequence":"additional","affiliation":[{"name":"University of British Columbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Ji","sequence":"additional","affiliation":[{"name":"Tsinghua Univeristy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,5,9]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Ahmed El Alaoui and Michael W. Mahoney. 2014. Fast randomized kernel methods with statistical guarantees. arXiv Preprint arXiv:1411.0306 (2014). Ahmed El Alaoui and Michael W. Mahoney. 2014. 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In Advances in Neural Information Processing Systems. 521--528."},{"volume-title":"Proceedings of the 31st International Conference on Machine Learning. 566--574","author":"Hsieh C.-J.","key":"e_1_2_1_9_1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6853587"},{"key":"e_1_2_1_12_1","unstructured":"Thorsten Joachims. 1999b. SVM-Light Support Vector Machine. http:\/\/svmlight.joachims.org\/. Thorsten Joachims. 1999b. SVM-Light Support Vector Machine. http:\/\/svmlight.joachims.org\/."},{"key":"e_1_2_1_13_1","unstructured":"Mayanka Katyal and Atul Mishra. 2014. A comparative study of load balancing algorithms in cloud computing environment. arXiv Preprint arXiv:1403.6918 (2014). Mayanka Katyal and Atul Mishra. 2014. A comparative study of load balancing algorithms in cloud computing environment. arXiv Preprint arXiv:1403.6918 (2014)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/ietisy\/e89-d.9.2533"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139176224"},{"volume-title":"Proceedings of the International Conference on Machine Learning.","year":"2013","author":"Le Quoc","key":"e_1_2_1_16_1"},{"volume-title":"Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. 1098--1106","year":"2015","author":"Yang Zichao","key":"e_1_2_1_17_1"},{"key":"e_1_2_1_18_1","unstructured":"Aditya Krishna Menon. 2009. Large-scale support vector machines: Algorithms and theory. Research Exam University of California San Diego. 1--17. Aditya Krishna Menon. 2009. Large-scale support vector machines: Algorithms and theory. 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