Thematische Bibliographien / Hidden Markov models indexed by trees

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  1. Zeitschriftenartikel
  2. Dissertationen
  3. Buchteile
  4. Konferenzberichte

Auswahl der wissenschaftlichen Literatur zum Thema „Hidden Markov models indexed by trees“

Autor: Grafiati
Veröffentlicht am 14. Dezember 2024
Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Hidden Markov models indexed by trees"

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Herrera, F., U. A. Rozikov, and M. V. Velasco. "Ising models with hidden Markov structure: applications to probabilistic inference in machine learning." Journal of Statistical Mechanics: Theory and Experiment 2025, no. 7 (2025): 073201. https://doi.org/10.1088/1742-5468/ade5f8.

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Abstract In this paper, we investigate tree-indexed Markov chains (Gibbs measures) defined by a Hamiltonian that couples two Ising layers: hidden spins s ( x ) ∈ { ± 1 } and observed spins σ ( x ) ∈ { ± 1 } on a Cayley tree. The Hamiltonian incorporates Ising interactions within each layer and site-wise emission couplings between layers, extending hidden Markov models to a bilayer Markov random field. Specifically, we explore translation-invariant Gibbs measures (TIGMs) of this Hamiltonian on Cayley trees. Under certain explicit conditions on the model’s parameters, we demonstrate that there c
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Huang, Huilin. "Strong Law of Large Numbers for Hidden Markov Chains Indexed by Cayley Trees." ISRN Probability and Statistics 2012 (September 23, 2012): 1–11. http://dx.doi.org/10.5402/2012/768657.

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We extend the idea of hidden Markov chains on lines to the situation of hidden Markov chains indexed by Cayley trees. Then, we study the strong law of large numbers for hidden Markov chains indexed by Cayley trees. As a corollary, we get the strong limit law of the conditional sample entropy rate.
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Milone, Diego H., Leandro E. Di Persia, and María E. Torres. "Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees." Pattern Recognition 43, no. 4 (2010): 1577–89. http://dx.doi.org/10.1016/j.patcog.2009.11.010.

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ANIGBOGU, J. C., and A. BELAÏD. "HIDDEN MARKOV MODELS IN TEXT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (1995): 925–58. http://dx.doi.org/10.1142/s0218001495000389.

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A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character
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Fredes, Luis, and Jean-François Marckert. "Invariant measures of interacting particle systems: Algebraic aspects." ESAIM: Probability and Statistics 24 (2020): 526–80. http://dx.doi.org/10.1051/ps/2020008.

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Consider a continuous time particle system ηt = (ηt(k), k ∈ 𝕃), indexed by a lattice 𝕃 which will be either ℤ, ℤ∕nℤ, a segment {1, ⋯ , n}, or ℤd, and taking its values in the set Eκ𝕃 where Eκ = {0, ⋯ , κ − 1} for some fixed κ ∈{∞, 2, 3, ⋯ }. Assume that the Markovian evolution of the particle system (PS) is driven by some translation invariant local dynamics with bounded range, encoded by a jump rate matrix ⊤. These are standard settings, satisfied by the TASEP, the voter models, the contact processes. The aim of this paper is to provide some sufficient and/or necessary conditions on the matri
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Narayana, Pradyumna, J. Ross Beveridge, and Bruce A. Draper. "Interacting Hidden Markov Models for Video Understanding." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (2018): 1855020. http://dx.doi.org/10.1142/s0218001418550200.

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People, cars and other moving objects in videos generate time series data that can be labeled in many ways. For example, classifiers can label motion tracks according to the object type, the action being performed, or the trajectory of the motion. These labels can be generated for every frame as long as the object stays in view, so object tracks can be modeled as Markov processes with multiple noisy observation streams. A challenge in video recognition is to recover the true state of the track (i.e. its class, action and trajectory) using Markov models without (a) counter-factually assuming th
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Durand, J. B., P. Goncalves, and Y. Guedon. "Computational Methods for Hidden Markov Tree Models—An Application to Wavelet Trees." IEEE Transactions on Signal Processing 52, no. 9 (2004): 2551–60. http://dx.doi.org/10.1109/tsp.2004.832006.

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Tso, Brandt, and Joe L. Tseng. "Multi-resolution semantic-based imagery retrieval using hidden Markov models and decision trees." Expert Systems with Applications 37, no. 6 (2010): 4425–34. http://dx.doi.org/10.1016/j.eswa.2009.11.086.

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Do, M. N. "Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models." IEEE Signal Processing Letters 10, no. 4 (2003): 115–18. http://dx.doi.org/10.1109/lsp.2003.809034.

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Maua, D. D., C. P. De Campos, A. Benavoli, and A. Antonucci. "Probabilistic Inference in Credal Networks: New Complexity Results." Journal of Artificial Intelligence Research 50 (July 28, 2014): 603–37. http://dx.doi.org/10.1613/jair.4355.

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Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted. In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence. We show that inferences under strong independence are NP-hard even in trees with binary variables except for a single ternary one. We prove that under epistemic irrelevance the pol
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Dissertationen zum Thema "Hidden Markov models indexed by trees"

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Weibel, Julien. "Graphons de probabilités, limites de graphes pondérés aléatoires et chaînes de Markov branchantes cachées." Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1031.

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Les graphes sont des objets mathématiques qui servent à modéliser tout type de réseaux, comme les réseaux électriques, les réseaux de communications et les réseaux sociaux. Formellement un graphe est composé d'un ensemble de sommets et d'un ensemble d'arêtes reliant des paires de sommets. Les sommets représentent par exemple des individus, tandis que les arêtes représentent les interactions entre ces individus. Dans le cas d'un graphe pondéré, chaque arête possède un poids ou une décoration pouvant modéliser une distance, une intensité d'interaction, une résistance. La modélisation de réseaux
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Lanka, Venkata Raghava Ravi Teja Lanka. "VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.

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Guo, Jia-Liang, and 郭家良. "Process Discovery using Rule-Integrated Trees Hidden Semi-Markov Models." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/456975.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>105<br>To predict or to explain? With the dramatical growth of the volume of information generated from various information systems, data science has become popular and important in recent years while machine learning algorithms provide a very strong support and foundation for various data applications. Many data applications are based on black-box models. For example, a fraud detection system can predict which person will default but we cannot understand how the system consider it’s fraud. While white-box models are easy to understand but have relatively poor pred
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Tu, Cheng-En, and 杜承恩. "Mandarin Tone Recognition based on Decision Trees and Hidden Markov Models." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/74449857537411484291.

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Buchteile zum Thema "Hidden Markov models indexed by trees"

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Oswald, Julie N., Christine Erbe, William L. Gannon, Shyam Madhusudhana, and Jeanette A. Thomas. "Detection and Classification Methods for Animal Sounds." In Exploring Animal Behavior Through Sound: Volume 1. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_8.

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AbstractClassification of the acoustic repertoires of animals into sound types is a useful tool for taxonomic studies, behavioral studies, and for documenting the occurrence of animals. Classification of acoustic repertoires enables the identification of species, age, gender, and individual identity, correlations between sound types and behavior, the identification of changes in vocal behavior over time or in response to anthropogenic noise, comparisons between the repertoires of populations living in different geographic regions and environments, and the development of software tools for auto
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Elakya, R., S. Surya, Abinaya G (c1edcca3-9bd8-40f6-a0f6-da223586be33, S. Shanthana, and T. Manoranjitham. "Unveiling the Depths." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6670-7.ch013.

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Deep-sea ecosystems, harbouring extraordinary biodiversity and geological wonders, remain largely unexplored due to their remote and inhospitable nature. Recent technological advancements, particularly in the realm of machine learning (ML), offer promising avenues for unravelling the mysteries of the deep and safeguarding its fragile ecosystems. This abstract presents an overview of the manifold applications of ML in deep-sea ecosystem analysis, focusing on key areas such as species identification, habitat characterization, and event detection. Primarily, ML algorithms, including Convolutional
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Konferenzberichte zum Thema "Hidden Markov models indexed by trees"

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Jin, Shaohua, Yongxue Wang, Huitao Liu, Ying Tian, and Hui Li. "Some Strong Limit Theorems for Hidden Markov Models Indexed by a Non-homogeneous Tree." In 2010 Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI). IEEE, 2010. http://dx.doi.org/10.1109/iitsi.2010.68.

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Milone, Diego H., Diego R. Tomassi, and Leandro E. Di Persia. "Signal denoising with hidden Markov models using hidden Markov trees as observation densities." In 2008 IEEE Workshop on Signal Processing for Machine Learning. IEEE, 2008. http://dx.doi.org/10.1109/mlsp.2008.4685509.

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Lacey, Arron, Jingjing Deng, and Xianghua Xie. "Protein classification using Hidden Markov models and randomised decision trees." In 2014 7th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2014. http://dx.doi.org/10.1109/bmei.2014.7002856.

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