{"type":"ScholarlyArticle","title":"Noise-Tolerant Conceptual Clustering","authors":[{"name":"Douglas H. Fisher","slug":"douglas-h-fisher","given":"Douglas H.","family":"Fisher","affiliation":""}],"abstract":"Fisher (1987a,b) introduced a performance task for conceptual clustering: flexible prediction of arbitrary attribute values, not simply the prediction of a single 'class' attribute . This paper extends earlier analysis by conside ring the effects of noise and other environmental factors . The degradation in flexible prediction accuracy that results from noise is mitigated by 'preferred' prediction points for individual attributes. Methods that identify these prediction points are inspired by pruning in learning from examples. We extend these noisetolerant techniques to untutored learning. In addition , prediction point preferences shed light on relationships between conceptual clustering, case-based, and default reasonmg.","doi":null,"url":"https://ijcai.org/proceedings/1989/132","pdf":"https://ijcai.org/proceedings/1989/0132.pdf","markdown":"https://ijcai.org/proceedings/1989/132.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/132","volume":{"slug":"1989","title":"Proceedings of the Eleventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1989","url":"https://ijcai.org/proceedings/1989/","year":1989,"location":"Detroit, Michigan, USA","dates":"August 20-25 1989","editors":"N. S. Sridharan","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":132,"pages":{"first":825,"last":829},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}