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The Hierarchical Hidden Markov Model: Analysis and Applications
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“Fine et al. [10] were the first to introduce the HHMM, generalizing the HMM by viewing each state as an autonomous probabilistic HMM model itself.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [10]
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“In this two-layer HHMM, the duration of a bottom-level state i ∈ ch(p), denoted as D p,i , follows a geometric distribution.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [10]
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Metadata
- Authors
- Fine, S., Singer, Y., & Tishby, N.
- Affiliations
- Hebrew University of Jerusalem (IL)
- AT&T (United States) (US)
- Venue
- Machine Learning
- Published
- 1998-07
- Volume
- 32
- Issue
- 1
- Pages
- 41-62
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1636244751
- Cited by
- 906
- References
- 33
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 19 · 2023: 26 · 2022: 39 · 2021: 36 · 2020: 51 · 2019: 34 · 2018: 30 · 2017: 39 · 2016: 33 · 2015: 31 · 2014: 53 · 2013: 36 · 2012: 43
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- hierarchical hidden Markov models, unsupervised parameter estimation, Baum-Welch algorithm, cursive script, natural language modeling
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