Hidden semi-Markov models
By Shunzheng Yu. Artificial Intelligence, 2010
640 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Oceanography, Signal Processing
View Paper: https://www.semanticscholar.org/paper/705da905174287789c57013c5d53beb22ba2f3a7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Gerhard Neumann
- Jan Peters
- Chelsea Finn
- J. Bagnell
- Yoshua Bengio
- J. Urgen Schmidhuber
In Oceanography
- Lionel Tarassenko
- Donghua Zhou
- Paul Weng
- A. Miller
- John A. D. Aston
- Jan Peters
- A. Franks
- Pieter Abbeel
In Signal Processing
- Lionel Tarassenko
- Donghua Zhou
- Paul Weng
- A. Miller
- John A. D. Aston
- Jan Peters
- A. Franks
- Pieter Abbeel
Citations in the corpus
- Remaining useful life estimation - A review on the statistical data driven approaches (2011, positive): “Although a HSMM is more powerful than a HMM in modeling RUL estimation , a HSMM leads to more complex parameters identification problems ( Yu , 2009 ; Azimi et al .”
- An Algorithmic Perspective on Imitation Learning (2018, positive): “Recent work also uses Hidden Semi-Markov Models (HSMM) [Yu, 2010] to model more complex state duration distributions [Calinon et al.”
- Quantifying the uncertainty in change points (2012, positive): “We refer the reader to Yu (2010) for an overview of HSMMs. HSMMs can be seen as extensions of HMMs except that associated with each state is information regarding the duration spent in that state, for example a probability mass function defined over a possible set of durations.”
- Characterizing the spatial structure of defensive skill in professional basketball (2014, positive): “Modeling the defense with a hidden semi-Markov model, which allows the transition probabilities to vary as a function of the time spent in each state, would be an interesting avenue for future research [Limnios and Oprisan (2001), Yu (2010)].”
- Bayesian nonparametric hidden semi-Markov models (2012, neutral): “There are several approaches to hidden semi-Markov models (Murphy, 2002; Yu, 2010).”
- Gesture recognition for human-robot collaboration: A review (2017, neutral): “Others extended HMM algo-rithm into wider range of applications (Yu, 2010).”
- Logistic Regression-HSMM-Based Heart Sound Segmentation (2016, positive): “Gill et al. [22] were the first researchers to consider timing durations within HMMs for heart sound segmentation, incorporating the time to preceding and following amplitude peaks in the homomorphic envelope as input features into the HMM, which yielded high segmentation accuracy on a small dataset.”
- Explicit-Duration Markov Switching Models (2014, positive): “…the viewpoint in [Murphy, 2002], the original and currently standard approach to explicit-duration modelling [Ferguson, 1980, Rabiner, 1989, Ostendorf et al., 1996, Yu, 2010] considers duration variables d1:T and variables c1:T such that, e.g., ct = 1 at the end of the segment and ct = 2 otherwise.”
- Solving Hidden-Semi-Markov-Mode Markov Decision Problems (2014, positive): “This new model is to hidden semi-Markov models [17] what HM-MDPs are to hidden Makov models.”
- Cloud-enabled prognosis for manufacturing (2015, positive): “The number of observations produced while in one state is determined by the length of time spent in this state as shown in Fig 11 [118].”