{"type":"ScholarlyArticle","title":"Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints","authors":[{"name":"Takashi Washio","slug":"takashi-washio","given":"Takashi","family":"Washio","affiliation":""},{"name":"Hiroshi Motoda","slug":"hiroshi-motoda","given":"Hiroshi","family":"Motoda","affiliation":""}],"abstract":"SDS is a discovery system from numeric measurement data. It outperforms the existing systems in every aspect of search efficiency, noise tolerancy, credibility of the resulting equations and complexity of the target system that it can handle. The power of SDS comes from the use of the scale-types of the measurement data and mathematical property of identity by which to constrain the admissible solutions. Its algorithm is described with a complex working example and the performance comparison with other systems are discussed.","doi":null,"url":"https://ijcai.org/proceedings/1997/117","pdf":"https://ijcai.org/proceedings/1997/0117.pdf","markdown":"https://ijcai.org/proceedings/1997/117.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/117","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning","track":"Learning","number":117,"pages":{"first":810,"last":818},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}