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|Title:||A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies||Authors:||Chatzis, Sotirios P.
Papadourakis, George M.
|Keywords:||Pattern Recognition;Computer Graphics;Image processing and computer vision;User interfaces and human computer interaction;Information systems applications (incl. Internet);Computational biology/bioinformatics||Category:||Computer and Information Sciences||Field:||Natural Sciences||Issue Date:||Dec-2014||Publisher:||Springer International Publishing||Source:||Advances in Visual Computing, 10th International Symposium, ISVC, Proceedings, Part I, 2014, Las Vegas, pages 51-62||metadata.dc.doi:||10.1007/978-3-319-14364-4_6||Abstract:||Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependencies, namely the sequence memoizer. We manage to derive training and inference algorithms for our model with computational costs identical to simple first-order HMMs, despite its entailed infinitely-long time-dependencies, by employing a mean-field-like approximation. The efficacy of our proposed model is experimentally demonstrated.||Description:||International Journal on Artificial Intelligence Tools, 2016, Volume 25, Issue 5, Article number 1640001||ISBN:||978-3-319-14363-7
|DOI:||10.1007/978-3-319-14364-4_6||Rights:||© Springer||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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