Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/13989
Title: | A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies |
Authors: | Chatzis, Sotirios P. Kosmopoulos, Dimitrios I. Papadourakis, George M. |
Major Field of Science: | Engineering and Technology |
Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering |
Keywords: | Markov processes;Speech recognition;Hidden Markov models |
Issue Date: | 1-Oct-2016 |
Source: | International Journal on Artificial Intelligence Tools, 2016, vol. 25, no. 5 |
Volume: | 25 |
Issue: | 5 |
Journal: | International Journal on Artificial Intelligence Tools |
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. |
ISSN: | 02182130 |
DOI: | 10.1142/S0218213016400017 |
Rights: | © World Scientific |
Type: | Article |
Affiliation : | Cyprus University of Technology University of Patras Hellenic Mediterranean University |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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