{"type":"ScholarlyArticle","title":"Efficient Online Learning and Prediction of Users' Desktop Actions","authors":[{"name":"Omid Madani","slug":"omid-madani","given":"Omid","family":"Madani","affiliation":""},{"name":"Hung Hai Bui","slug":"hung-hai-bui","given":"Hung Hai","family":"Bui","affiliation":""},{"name":"Eric Yeh","slug":"eric-yeh","given":"Eric","family":"Yeh","affiliation":""}],"abstract":"We investigate prediction of users' desktop activities in the Unix domain. The learning techniques we explore do not require explicit user teaching. We show that simple efficient many-class learning can perform well for action prediction, significantly improving over previously published results and baselines. This finding is promising for various human-computer interaction scenarios where a rich set of potentially predictive features is available, where there can be many different actions to predict, and where there can be considerable nonstationarity.Omid Madani, Hung Bui, Eric Yeh","doi":null,"url":"https://ijcai.org/proceedings/2009/244","pdf":"https://ijcai.org/proceedings/2009/0244.pdf","markdown":"https://ijcai.org/proceedings/2009/244.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/244","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":244,"pages":{"first":1457,"last":1461},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}