{"type":"ScholarlyArticle","title":"Learning Disjunction of Conjunctions","authors":[{"name":"Leslie G. Valiant","slug":"leslie-g-valiant","given":"Leslie G.","family":"Valiant","affiliation":""}],"abstract":"The question of wheth e r concepts expressible as disjun ctio ns of conjunctions can be learn e d from examples in polynomial time is investigated . Positive results are shown for significant subclasses that allow not only propositional predicates but also some relations . The algorithms are extend e d so as to be provably tolerant to a certain quantifiable error rate in the examples data . It is further shown that under certain restrictions on these subclasses th e l ear ning algorithms ar e well suited to implementation on neural networks of threshold elements. The possible importance of disjunctions of conjunctions as a knowledge repr ese ntation stems from th e observations that on the one hand humans appear t o like using it , and , on the other, that there is circumstantial evidence that significantly larger classes may not be learnable in polynomial time . An NP-completeness result corroborating the latter is also presented.","doi":null,"url":"https://ijcai.org/proceedings/1985/107","pdf":"https://ijcai.org/proceedings/1985/0107.pdf","markdown":"https://ijcai.org/proceedings/1985/107.md","bibtex":"https://ijcai.org/proceedings/1985/bibtex/107","volume":{"slug":"1985","title":"Proceedings of the Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1985","url":"https://ijcai.org/proceedings/1985/","year":1985,"location":"Los Angeles, California, USA","dates":"","editors":"Aravind Joshi","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning and Acquisition","track":"Learning and Acquisition","number":107,"pages":{"first":560,"last":565},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}