{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:25:52Z","timestamp":1740201952540,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>The predominant learning strategy for H(S)MMs is local search heuristics, of which the Baum-Welch\/ expectation maximization (EM) algorithm is mostly used. It is an iterative learning procedure starting with a predefined topology and randomly-chosen initial parameters. However, state-of-the-art approaches based on arbitrarily defined state numbers and parameters can cause the risk of falling into a local optima and a low convergence speed with enormous number of iterations in learning which is computationally expensive. For models with persistent states, i.e. states with high self-transition probabilities, we propose a segmentation-based identification approach used as a pre-identification step to approximately estimate parameters based on segmentation and clustering techniques. The identified parameters serve as input of the Baum-Welch algorithm. Moreover, the proposed approach identifies automatically the state numbers. Experimental results conducted on both synthetic and real data show that the segmentation-based identification approach can identify H(S)MMs more accurately and faster than the current Baum-Welch algorithm.<\/jats:p>","DOI":"10.3233\/978-1-61499-421-3-171","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:21:00Z","timestamp":1740133260000},"source":"Crossref","is-referenced-by-count":0,"title":["Effective and Efficient Identification of Persistent-state Hidden (semi-) Markov Models"],"prefix":"10.3233","author":[{"family":"Liu Tingting","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Lemeire Jan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","STAIRS 2014"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:21:33Z","timestamp":1740133293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=264&spage=171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-421-3-171","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2014]]}}}