{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:21:25Z","timestamp":1783578085425,"version":"3.55.0"},"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":[[2023,8]]},"abstract":"<jats:p>We investigate the generalization properties of a self-training algorithm with halfspaces. The approach learns a list of halfspaces iteratively from labeled and unlabeled training data, in which each iteration consists of two steps: exploration and pruning. In the exploration phase, the halfspace is found sequentially by maximizing the unsigned-margin among  unlabeled examples and then assigning pseudo-labels to those that have a distance higher than the current threshold. These pseudo-labels are allegedly corrupted by noise. The training set is then augmented with noisy pseudo-labeled examples, and a new classifier is trained.  This process is repeated until no more unlabeled examples remain for pseudo-labeling. In the pruning phase, pseudo-labeled samples that have a distance to the last halfspace greater than the associated  unsigned-margin are then discarded. We prove that the misclassification error of the resulting sequence of classifiers is bounded and show that the resulting semi-supervised approach never degrades performance compared to the  classifier learned using only the initial labeled training set. Experiments carried out on a variety of benchmarks demonstrate the efficiency of the proposed approach compared to state-of-the-art methods.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/420","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:31:30Z","timestamp":1691728290000},"page":"3777-3785","source":"Crossref","is-referenced-by-count":3,"title":["Generalization Guarantees of Self-Training of Halfspaces under Label Noise Corruption"],"prefix":"10.24963","author":[{"given":"Lies","family":"Hadjadj","sequence":"first","affiliation":[{"name":"Computer Science Laboratory (LIG), Universit\u00e9 Grenoble Alpes, Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massih-Reza","family":"Amini","sequence":"additional","affiliation":[{"name":"Computer Science Laboratory (LIG), Universit\u00e9 Grenoble Alpes, Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sana","family":"Louhichi","sequence":"additional","affiliation":[{"name":"Department of Statistics (LJK), Universit\u00e9 Grenoble Alpes, Grenoble, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:48:12Z","timestamp":1691729292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/420"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/420","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}