The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
An efficient forward-backward algorithm for an explicit-duration hidden Markov model
Abstract
Existing algorithms for estimating the model parameters of an explicit-duration hidden Markov model (HMM) usually require computations as large as O((MD/sup 2/ + M/sup 2/)T) or O(M/sup 2/ DT), where M is the number of states; D is the maximum possible interval between state transitions; and T is the period of observations used to estimate the model parameters. Because of such computational requirements, these algorithms are not practical when we wish to construct an HMM model with large state space and large explicit state duration and process a large amount of measurement data to obtain high accuracy. We propose a new forward-backward algorithm whose computational complexity is only O((MD + M/sup 2/)T), a reduction by almost a factor of D when D > M and whose memory requirement is O(MT). As an application example, we discuss an HMM characterization of access traffic observed at a large-scale Web site: we formulate the Web access pattern in terms of an HMM with explicit duration and estimate the model parameters using our algorithm.
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In the citing paper
“The most common choice for modeling the state duration is the multinomial [18,21,34,43] due to its simplicity.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [43]
In the citing paper
“By taking advantage of the determinism of m t (i.e.conditionally on a given state, m t+1 = m t -1), the complexity can be reduced to O (|Q | 2 M T ), or even better to O ((|Q |M +|Q | 2 )T )”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [43]
Citations
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A hidden semi-Markov model for web workload self-similarity
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Self-citationMonitoring the Application-Layer DDoS Attacks for Popular Websites
Xie, Y., & Yu, S. (2008) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2008.925628 · Cited by 267
Self-citationA Large-Scale Hidden Semi-Markov Model for Anomaly Detection on User Browsing Behaviors
Xie, Y., & Yu, S. (2008) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2008.923716 · Cited by 222
Self-citationRobust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis
Yamagishi, J., Nose, T., Zen, H., Ling, Z.-H., Toda, T., Tokuda, K., King, S., & Renals, S. J. (2009) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2009.2016394 · Cited by 178
Stylized facts of financial time series and hidden semi-Markov models
Bulla, J., & Bulla, I. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.07.021 · Cited by 161
Metadata
- Authors
- Yu, S.-Z., & Kobayashi, H.
- Affiliations
- Princeton University (US)
- Venue
- IEEE Signal Processing Letters
- Published
- 2003-01
- Volume
- 10
- Issue
- 1
- Pages
- 11-14
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2153331007
- Cited by
- 226
- References
- 10
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 11 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 11 · 2019: 12 · 2018: 11 · 2017: 16 · 2016: 10 · 2015: 12 · 2014: 19 · 2013: 13 · 2012: 12
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Letters (publishedVersion)
- Funders
- George Mason University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- forward-backward algorithm, parameter estimation, computational complexity reduction, space complexity, web traffic modeling, state duration
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