{"type":"ScholarlyArticle","title":"Nonparametric Online Machine Learning with Kernels","authors":[{"name":"Khanh Nguyen","slug":"khanh-nguyen","given":"Khanh","family":"Nguyen","affiliation":"Centre for Pattern Recognition and Data Analytics , Deakin University , Australia"}],"abstract":"Max-margin and kernel methods are dominant approaches to solve many tasks in machine learning. However, the paramount question is how to solve model selection problem in these methods. It becomes urgent in online learning context. Grid search is a common approach, but it turns out to be highly problematic in real-world applications. Our approach is to view max-margin and kernel methods under a Bayesian setting, then use Bayesian inference tools to learn model parameters and infer hyper-parameters in principle ways for both batch and online setting.","doi":"10.24963/ijcai.2017/758","url":"https://ijcai.org/proceedings/2017/758","pdf":"https://ijcai.org/proceedings/2017/0758.pdf","markdown":"https://ijcai.org/proceedings/2017/758.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/758","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":758,"pages":{"first":5197,"last":5198},"keywords":["Artificial Intelligence: computer science","Artificial Intelligence: machine learning","Artificial Intelligence: uncertainty in artificial intelligence"],"videos":[],"references":[{"position":1,"raw":"[Gönen and Alpaydın, 2011] Mehmet Gönen and Ethem Al- paydın. 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