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https://hdl.handle.net/20.500.14279/8582
Τίτλος: | A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies |
Συγγραφείς: | Chatzis, Sotirios P. Kosmopoulos, Dimitrios I. Papadourakis, George M. |
Major Field of Science: | Engineering and Technology |
Field Category: | Computer and Information Sciences |
Λέξεις-κλειδιά: | Pattern Recognition;Computer Graphics;Image processing and computer vision;User interfaces and human computer interaction;Information systems applications (incl. Internet);Computational biology/bioinformatics |
Ημερομηνία Έκδοσης: | Δεκ-2014 |
Πηγή: | International Symposium on Visual Computing ISVC 2014: Advances in Visual Computing, pp. 51-62 |
Conference: | International Symposium on Visual Computing (ISVC) |
Περίληψη: | 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. |
ISBN: | 978-3-319-14363-7 978-3-319-14364-4 (online) |
DOI: | 10.1007/978-3-319-14364-4_6 |
Rights: | © Springer |
Type: | Conference Papers |
Affiliation: | Cyprus University of Technology University of Patras Hellenic Mediterranean University |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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