Hidden Markov processes
By Yariv Ephraim, Neri Merhav. IEEE Transactions on Information Theory, 2002
903 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/5c239544407f4a9569b09d8dfa2927e9fef6219c
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
- John A. Rhodes
- Catherine Matias
- Mohamed S. Kamel
- C. Thompson
- F. Karray
- Pradeep K. Khosla
- Jeffrey H. Reed
- P. Thomson
In Signal Processing
- Mohamed S. Kamel
- F. Karray
- Moataz M. H. El Ayadi
- John A. Rhodes
- Catherine Matias
- Elizabeth S. Allman
- Jan Bulla
Citations in the corpus
- Survey on speech emotion recognition: Features, classification schemes, and databases (2011, methodology): “Fortunately, very efficient algorithms have been proposed for the calculation of the likelihood function in a time of order OðKTÞ such as the forward recursion and the backward recursion algorithms (for details about these algorithms, the reader is referred to [102,39]).”
- Hidden Markov models with t components. Increased persistence and other aspects (2011, background): “For a short overview, the article of Ephraim and Merhav (2002) and the sources mentioned therein provide a good introduction.”
- Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio (2007, background): “A hidden Markov process (HMP) [9] is a doubly stochastic process in which the generation of observation symbols depends on the emission properties of the states.”
- Predictive Robot Programming: Theoretical and Experimental Analysis (2004, background): “We have therefore focused our attention on a low-complexity algorithm that produces the “right” waypoint at the “right” time, rather than asymptotic sequence guarantees such as those described by Ephraim and Merhav (2002).”
- Use of exponential hidden Markov models for modelling pavement deterioration (2013, background): “HMMs have been significantly studied and broadly applied in the field of image processing and speech recognition (Ephraim and Merhav 2002 ).”
- Space–time modelling of precipitation by using a hidden Markov model and censored Gaussian distributions (2009, background): “An important by-product of this key algorithm is the evaluation of the incomplete likelihood (see Ephraim and Merhav (2002) for example).”
- Identifiability of parameters in latent structure models with many observed variables (2008, methodology): “We refer to [5, 16] for more details on HMMs.”
- Biomedical text mining and its applications in cancer research (2013, neutral): “Machine learning approaches are now used widely for named entity recognition, e.g., Hidden Markov Models (HMM) [18], Support Vector Machines (SVMs) [19], Conditional Random Fields (CRFs) [20], and Maximum Entropy (ME) [21].”
- Hidden semi-Markov models (2010, neutral): “Then the order estimators given in Ephraim and Merhav [57] can be used in the selection of the model order.”