{"type":"ScholarlyArticle","title":"Selective Supervision: Guiding Supervised Learning with Decision-Theoretic Active Learning","authors":[{"name":"Ashish Kapoor","slug":"ashish-kapoor","given":"Ashish","family":"Kapoor","affiliation":""},{"name":"Eric Horvitz","slug":"eric-horvitz","given":"Eric","family":"Horvitz","affiliation":""},{"name":"Sumit Basu","slug":"sumit-basu","given":"Sumit","family":"Basu","affiliation":""}],"abstract":"An inescapable bottleneck with learning from large data sets is the high cost of labeling training data. Unsupervised learning methods have promised to lower the cost of tagging by leveraging notions of similarity among data points to assign tags. However, unsupervised and semi-supervised learning techniques often provide poor results due to errors in estimation. We look at methods that guide the allocation of human effort for labeling data so as to get the greatest boosts in discriminatory power with increasing amounts of work. We focus on the application of value of information to Gaussian Process classifiers and explore the effectiveness of the method on the task of classifying voice messages.","doi":null,"url":"https://ijcai.org/proceedings/2007/141","pdf":"https://ijcai.org/proceedings/2007/0141.pdf","markdown":"https://ijcai.org/proceedings/2007/141.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/141","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":141,"pages":{"first":877,"last":881},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}