{"type":"ScholarlyArticle","title":"Empirical Bias for Version Space","authors":[{"name":"Jacques Nicolas","slug":"jacques-nicolas","given":"Jacques","family":"Nicolas","affiliation":""}],"abstract":"The ability to generalize remains one of the central issues of concept learning. A general generalization algorithm -the Candidate Elimination Algorithmexists but practical applications of this algorithm are still limited, due to its low convergence. The issue has shifted to the design of a useful \"bias\" limiting the size of the Version Space. This paper proposes a new kind of bias, called empirical bias, and a new general algorithm, ICE, for generalization in presence of bias. This proposition is founded on the concept of focus set, which provides a very flexible way to express expectations or constraints on the space of generalizations.","doi":null,"url":"https://ijcai.org/proceedings/1991/98","pdf":"https://ijcai.org/proceedings/1991/0098.pdf","markdown":"https://ijcai.org/proceedings/1991/98.md","bibtex":"https://ijcai.org/proceedings/1991/bibtex/98","volume":{"slug":"1991","title":"Proceedings of the Twelfth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1991","url":"https://ijcai.org/proceedings/1991/","year":1991,"location":"Sydney, New South Wales, Australia","dates":"24-30 August 1991","editors":"John Mylopoulos and Ray Reiter","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning Be Knowledge Acquisition","track":"Learning Be Knowledge Acquisition","number":98,"pages":{"first":671,"last":676},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}