{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T10:11:35Z","timestamp":1725617495097},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>In multi-class support vector machine (MSVM) for classification, one core issue is to regularize the coefficient vectors to reduce overfitting. Various regularizers have been proposed such as L2, L1, and trace norm. In this paper, we introduce a new type of regularization approach -- angular regularization, that encourages the coefficient vectors to have larger angles such that class regions can be widen to flexibly accommodate unseen samples. We propose a novel angular regularizer based on the singular values of the coefficient matrix, where the uniformity of singular values reduces the correlation among different classes and drives the angles between coefficient vectors to increase. In generalization error analysis, we show that decreasing this regularizer effectively reduces generalization error bound. On various datasets, we demonstrate the efficacy of the regularizer in reducing overfitting.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/296","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"2131-2137","source":"Crossref","is-referenced-by-count":4,"title":["Improving the Generalization Performance of Multi-class SVM via Angular Regularization"],"prefix":"10.24963","author":[{"given":"Jianxin","family":"Li","sequence":"first","affiliation":[{"name":"Beihang University"}]},{"given":"Haoyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University"}]},{"given":"Pengtao","family":"Xie","sequence":"additional","affiliation":[{"name":"Machine Learning Department, Carnegie Mellon University"},{"name":"Petuum Inc, USA"}]},{"given":"Yingchun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:53:15Z","timestamp":1501242795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/296"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/296","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}