{"type":"ScholarlyArticle","title":"Incremental Construction of Structured Hidden Markov Models","authors":[{"name":"Ugo Galassi","slug":"ugo-galassi","given":"Ugo","family":"Galassi","affiliation":""},{"name":"Attilio Giordana","slug":"attilio-giordana","given":"Attilio","family":"Giordana","affiliation":""},{"name":"Lorenza Saitta","slug":"lorenza-saitta","given":"Lorenza","family":"Saitta","affiliation":""}],"abstract":"This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Models and are well suited to problems of process/user profiling. The learning algorithm is unsupervised, and follows a mixed bottom-up/top-down strategy, in which elementary facts in the sequences (motifs) are progressively grouped, thus building up the abstraction hierarchy of a S-HMM, layer after layer. The algorithm is validated on a suite of artificial datasets, where the challenge for the learning algorithm is to reconstruct the model that generated the data. Then, an application to a real problem of molecular biology is briefly described.","doi":null,"url":"https://ijcai.org/proceedings/2007/128","pdf":"https://ijcai.org/proceedings/2007/0128.pdf","markdown":"https://ijcai.org/proceedings/2007/128.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/128","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":128,"pages":{"first":798,"last":802},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}