{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:38:47Z","timestamp":1760708327044,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2011,8,21]],"date-time":"2011-08-21T00:00:00Z","timestamp":1313884800000},"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":[],"published-print":{"date-parts":[[2011,8,21]]},"DOI":"10.1145\/2020408.2020420","type":"proceedings-article","created":{"date-parts":[[2011,8,31]],"date-time":"2011-08-31T15:22:45Z","timestamp":1314804165000},"page":"24-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Trading representability for scalability"],"prefix":"10.1145","author":[{"given":"Zhuang","family":"Wang","sequence":"first","affiliation":[{"name":"Siemens Corporate Research, Princeton, USA"}]},{"given":"Nemanja","family":"Djuric","sequence":"additional","affiliation":[{"name":"Temple University, Philadelphia,, USA"}]},{"given":"Koby","family":"Crammer","sequence":"additional","affiliation":[{"name":"The Technion, Haifa, Israel"}]},{"given":"Slobodan","family":"Vucetic","sequence":"additional","affiliation":[{"name":"Temple University, Philadelphia, USA"}]}],"member":"320","published-online":{"date-parts":[[2011,8,21]]},"reference":[{"key":"e_1_3_2_1_1_1","author":"Aiolli F.","year":"2005","unstructured":"F. Aiolli and A. Sperduti . Multi-class classification with multi-prototype support vector machines. Journal of Machine Learning Research , 2005 . F. Aiolli and A. Sperduti. Multi-class classification with multi-prototype support vector machines. Journal of Machine Learning Research, 2005.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_2_1","author":"Bordes A.","year":"2009","unstructured":"A. Bordes , L. Bottou , and P. Gallinari . Sgd-qn: careful quasi-newton stochastic gradient descent. Journal of Machine Learning Research , 2009 . A. Bordes, L. Bottou, and P. Gallinari. Sgd-qn: careful quasi-newton stochastic gradient descent. Journal of Machine Learning Research, 2009.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_3_1","author":"Bordes A.","year":"2005","unstructured":"A. Bordes , S. Ertekin , J. Weston , and L. Bottou . Fast kernel classifiers for online and active learning. Journal of Machine Learning Research , 2005 . A. Bordes, S. Ertekin, J. Weston, and L. Bottou. Fast kernel classifiers for online and active learning. Journal of Machine Learning Research, 2005.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_2_1_5_1","volume-title":"Journal of Machine Learning Research","author":"Chang Y.-W.","year":"2010","unstructured":"Y.-W. Chang , K.-W. C. C.-J. Hsie and, M. Ringgaard , and C.-J. Lin . Training and testing low-degree polynomial data mappings via linear svm . Journal of Machine Learning Research , 2010 . Y.-W. Chang, K.-W. C. C.-J. Hsie and, M. Ringgaard, and C.-J. Lin. Training and testing low-degree polynomial data mappings via linear svm. Journal of Machine Learning Research, 2010."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143870"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_3_2_1_8_1","author":"Crammer K.","year":"2001","unstructured":"K. Crammer and Y. Singer . On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research , 2001 . K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2001.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390208"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150429"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.830991"},{"key":"e_1_3_2_1_12_1","volume-title":"Training invariant support vector machines using selective sampling. Large Scale Kernel Machines","author":"Loosli G.","year":"2007","unstructured":"G. Loosli , S. Canu , and L. Bottou . Training invariant support vector machines using selective sampling. Large Scale Kernel Machines , Cam-bridge, MA , MIT Press , 2007 . G. Loosli, S. Canu, and L. Bottou. Training invariant support vector machines using selective sampling. Large Scale Kernel Machines, Cam-bridge, MA, MIT Press, 2007."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553462"},{"key":"e_1_3_2_1_14_1","volume-title":"Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods - support vector learning","author":"Platt J.","year":"1998","unstructured":"J. Platt . Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods - support vector learning , MIT Press , 1998 . J. Platt. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods - support vector learning, MIT Press, 1998."},{"key":"e_1_3_2_1_15_1","volume-title":"Advance in Nueral Information Processing Systems","author":"Platt J.","year":"2000","unstructured":"J. Platt , N. Cristianini , and J. S. Taylor . Large margin dags for multiclass classification . In Advance in Nueral Information Processing Systems , 2000 . J. Platt, N. Cristianini, and J. S. Taylor. Large margin dags for multiclass classification. In Advance in Nueral Information Processing Systems, 2000."},{"key":"e_1_3_2_1_16_1","volume-title":"Advance in Nueral Information Processing Systems","author":"Rahimi A.","year":"2007","unstructured":"A. Rahimi and B. Recht . Random features for large-scale kernel machines . In Advance in Nueral Information Processing Systems , 2007 . A. Rahimi and B. Recht. Random features for large-scale kernel machines. In Advance in Nueral Information Processing Systems, 2007."},{"key":"e_1_3_2_1_17_1","volume-title":"Logarithmic regret algorithms for strongly convex repeated games (technical report)","author":"Shalev-Shwartz S.","year":"2007","unstructured":"S. Shalev-Shwartz and Y. Singer . Logarithmic regret algorithms for strongly convex repeated games (technical report) . The Hebrew University , 2007 . S. Shalev-Shwartz and Y. Singer. Logarithmic regret algorithms for strongly convex repeated games (technical report). The Hebrew University, 2007."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273598"},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning","author":"Sonnenburg S.","year":"2010","unstructured":"S. Sonnenburg and V. Franc . Coffin : a computational framework for linear svms . In International Conference on Machine Learning , 2010 . S. Sonnenburg and V. Franc. Coffin : a computational framework for linear svms. In International Conference on Machine Learning, 2010."},{"key":"e_1_3_2_1_20_1","volume-title":"Journal of Machine Learning Research","author":"Steinwart I.","year":"2003","unstructured":"I. Steinwart . Sparseness of support vector machines . Journal of Machine Learning Research , 2003 . I. Steinwart. Sparseness of support vector machines. Journal of Machine Learning Research, 2003."},{"key":"e_1_3_2_1_21_1","author":"Teo C.","year":"2010","unstructured":"C. Teo , S. V. N. Vishwanathan , A. J. Smola , and Q. V. Le . Bundle methods for regularized risk minimization. Journal of Machine Learning Research , 2010 . C. Teo, S. V. N. Vishwanathan, A. J. Smola, and Q. V. Le. Bundle methods for regularized risk minimization. Journal of Machine Learning Research, 2010.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_22_1","volume-title":"Journal of Machine Learning Research","author":"Tsang I. W.","year":"2005","unstructured":"I. W. Tsang , J. T. Kwok , and P.-M. Cheung . Core vector machines: fast svm training on very large data sets . Journal of Machine Learning Research , 2005 . I. W. Tsang, J. T. Kwok, and P.-M. Cheung. Core vector machines: fast svm training on very large data sets. Journal of Machine Learning Research, 2005."},{"key":"e_1_3_2_1_23_1","volume-title":"Simplesvm. In International Conference on Machine Learning","author":"Vishwanathan S. V. N.","year":"2003","unstructured":"S. V. N. Vishwanathan , A. J. Smola , and M. N. Murty . Simplesvm. In International Conference on Machine Learning , 2003 . S. V. N. Vishwanathan, A. J. Smola, and M. N.Murty. Simplesvm. In International Conference on Machine Learning, 2003."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","DOI":"10.5772\/217","volume-title":"International Conference on Machine Learning","author":"Wang Z.","year":"2010","unstructured":"Z. Wang , K. Crammer , and S. Vucetic . Multi-class pegasos on a budget . In International Conference on Machine Learning , 2010 . Z. Wang, K. Crammer, and S.Vucetic. Multi-class pegasos on a budget. In International Conference on Machine Learning, 2010."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/sam.v3:3"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835910"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015332"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2009.29"}],"event":{"name":"KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"San Diego California USA","acronym":"KDD '11"},"container-title":["Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2020408.2020420","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2020408.2020420","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:48:15Z","timestamp":1750240095000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2020408.2020420"}},"subtitle":["adaptive multi-hyperplane machine for nonlinear classification"],"short-title":[],"issued":{"date-parts":[[2011,8,21]]},"references-count":28,"alternative-id":["10.1145\/2020408.2020420","10.1145\/2020408"],"URL":"https:\/\/doi.org\/10.1145\/2020408.2020420","relation":{},"subject":[],"published":{"date-parts":[[2011,8,21]]},"assertion":[{"value":"2011-08-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}