{"type":"ScholarlyArticle","title":"Inductive Learning from Good Examples","authors":[{"name":"Charles X. Ling","slug":"charles-x-ling","given":"Charles X.","family":"Ling","affiliation":""}],"abstract":"We study what kind of data may ease the computational complexity of learning of Horn clause theories (in Gold's paradigm) and Boolean functions (in PAC-learning paradigm). We give several definitions of good data (basic and generative representative sets), and develop data-driven algorithms that learn faster from good examples, and degenerate to learn in the limit from the ''worst\" possible examples. We show that Horn clause theories, kterm DNF and general DNF Boolean functions are polynomially learnable from generative representative presentations.","doi":null,"url":"https://ijcai.org/proceedings/1991/110","pdf":"https://ijcai.org/proceedings/1991/0110.pdf","markdown":"https://ijcai.org/proceedings/1991/110.md","bibtex":"https://ijcai.org/proceedings/1991/bibtex/110","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":110,"pages":{"first":751,"last":755},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}