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Title: A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies
Authors: Chatzis, Sotirios P. 
Kosmopoulos, Dimitrios 
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
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
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
978-3-319-14364-4 (online)
ISSN: 10.1007/978-3-319-14364-4_6
DOI: 10.1007/978-3-319-14364-4_6
Rights: © Springer
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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