{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semi-supervised-online-structure-learning-for","title":"Semi-Supervised Online Structure Learning for Composite Event Recognition","arxiv_id":"1803.00546","date":"2018-03-01","proceeding":null,"authors":["Evangelos Michelioudakis","Alexander Artikis","Georgios Paliouras"],"abstract":"Online structure learning approaches, such as those stemming from Statistical\nRelational Learning, enable the discovery of complex relations in noisy data\nstreams. However, these methods assume the existence of fully-labelled training\ndata, which is unrealistic for most real-world applications. We present a novel\napproach for completing the supervision of a semi-supervised structure learning\ntask. We incorporate graph-cut minimisation, a technique that derives labels\nfor unlabelled data, based on their distance to their labelled counterparts. In\norder to adapt graph-cut minimisation to first order logic, we employ a\nsuitable structural distance for measuring the distance between sets of logical\natoms. The labelling process is achieved online (single-pass) by means of a\ncaching mechanism and the Hoeffding bound, a statistical tool to approximate\nglobally-optimal decisions from locally-optimal ones. We evaluate our approach\non the task of composite event recognition by using a benchmark dataset for\nhuman activity recognition, as well as a real dataset for maritime monitoring.\nThe evaluation suggests that our approach can effectively complete the missing\nlabels and eventually, improve the accuracy of the underlying structure\nlearning system.","url_abs":"http://arxiv.org/abs/1803.00546v2","url_pdf":"http://arxiv.org/pdf/1803.00546v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semi-supervised-online-structure-learning-for","repo_url":"https://github.com/nkatzz/OLED","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"semi-supervised-online-structure-learning-for","repo_url":"https://github.com/anskarl/LoMRF","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"},{"task_slug":"missing-labels","task_name":"Missing Labels"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}