Literatura académica sobre el tema "Hidden Markov models indexed by trees"
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Artículos de revistas sobre el tema "Hidden Markov models indexed by trees"
Huang, Huilin. "Strong Law of Large Numbers for Hidden Markov Chains Indexed by Cayley Trees". ISRN Probability and Statistics 2012 (23 de septiembre de 2012): 1–11. http://dx.doi.org/10.5402/2012/768657.
Texto completoMilone, Diego H., Leandro E. Di Persia y María E. Torres. "Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees". Pattern Recognition 43, n.º 4 (abril de 2010): 1577–89. http://dx.doi.org/10.1016/j.patcog.2009.11.010.
Texto completoANIGBOGU, J. C. y A. BELAÏD. "HIDDEN MARKOV MODELS IN TEXT RECOGNITION". International Journal of Pattern Recognition and Artificial Intelligence 09, n.º 06 (diciembre de 1995): 925–58. http://dx.doi.org/10.1142/s0218001495000389.
Texto completoNarayana, Pradyumna, J. Ross Beveridge y Bruce A. Draper. "Interacting Hidden Markov Models for Video Understanding". International Journal of Pattern Recognition and Artificial Intelligence 32, n.º 11 (24 de julio de 2018): 1855020. http://dx.doi.org/10.1142/s0218001418550200.
Texto completoFredes, Luis y 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.
Texto completoDurand, J. B., P. Goncalves y Y. Guedon. "Computational Methods for Hidden Markov Tree Models—An Application to Wavelet Trees". IEEE Transactions on Signal Processing 52, n.º 9 (septiembre de 2004): 2551–60. http://dx.doi.org/10.1109/tsp.2004.832006.
Texto completoTso, Brandt y Joe L. Tseng. "Multi-resolution semantic-based imagery retrieval using hidden Markov models and decision trees". Expert Systems with Applications 37, n.º 6 (junio de 2010): 4425–34. http://dx.doi.org/10.1016/j.eswa.2009.11.086.
Texto completoDo, M. N. "Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models". IEEE Signal Processing Letters 10, n.º 4 (abril de 2003): 115–18. http://dx.doi.org/10.1109/lsp.2003.809034.
Texto completoMaua, D. D., C. P. De Campos, A. Benavoli y A. Antonucci. "Probabilistic Inference in Credal Networks: New Complexity Results". Journal of Artificial Intelligence Research 50 (28 de julio de 2014): 603–37. http://dx.doi.org/10.1613/jair.4355.
Texto completoSegers, Johan. "One- versus multi-component regular variation and extremes of Markov trees". Advances in Applied Probability 52, n.º 3 (septiembre de 2020): 855–78. http://dx.doi.org/10.1017/apr.2020.22.
Texto completoTesis sobre el tema "Hidden Markov models indexed by trees"
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.
Texto completoGraphs are mathematical objects used to model all kinds of networks, such as electrical networks, communication networks, and social networks. Formally, a graph consists of a set of vertices and a set of edges connecting pairs of vertices. The vertices represent, for example, individuals, while the edges represent the interactions between these individuals. In the case of a weighted graph, each edge has a weight or a decoration that can model a distance, an interaction intensity, or a resistance. Modeling real-world networks often involves large graphs with a large number of vertices and edges.The first part of this thesis is dedicated to introducing and studying the properties of the limit objects of large weighted graphs : probability-graphons. These objects are a generalization of graphons introduced and studied by Lovász and his co-authors in the case of unweighted graphs. Starting from a distance that induces the weak topology on measures, we define a cut distance on probability-graphons. We exhibit a tightness criterion for probability-graphons related to relative compactness in the cut distance. Finally, we prove that this topology coincides with the topology induced by the convergence in distribution of the sampled subgraphs. In the second part of this thesis, we focus on hidden Markov models indexed by trees. We show the strong consistency and asymptotic normality of the maximum likelihood estimator for these models under standard assumptions. We prove an ergodic theorem for branching Markov chains indexed by trees with general shapes. Finally, we show that for a stationary and reversible chain, the line graph is the tree shape that induces the minimal variance for the empirical mean estimator among trees with a given number of vertices
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.
Texto completoGuo, Jia-Liang y 郭家良. "Process Discovery using Rule-Integrated Trees Hidden Semi-Markov Models". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/456975.
Texto completo國立中山大學
資訊管理學系研究所
105
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 predictive performance. Hence, in this thesis, we propose a novel grafted tree algorithm to integrate trees of random forests. The model attempt to find a balance between a decision tree and a random forest. That is, the grafted tree have better interpretability and the performance than a single decision tree. With the decision tree is integrated from a random forest, it will be applied to Hidden semi-Markov models (HSMM) to build a Classification Tree Hidden Semi- Markov Model (CTHSMM) in order to discover underlying changes of a system. The experimental result shows that our proposed model RITHSMM is better than a simple decision tree based on Classification and Regression Trees and it can find more states/leaves so as to answer a kind of questions, “given a sequence of observable sequence, what are the most probable/relevant sequence of changes of a dynamic system?”.
Tu, Cheng-En y 杜承恩. "Mandarin Tone Recognition based on Decision Trees and Hidden Markov Models". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/74449857537411484291.
Texto completoCapítulos de libros sobre el tema "Hidden Markov models indexed by trees"
Oswald, Julie N., Christine Erbe, William L. Gannon, Shyam Madhusudhana y Jeanette A. Thomas. "Detection and Classification Methods for Animal Sounds". En Exploring Animal Behavior Through Sound: Volume 1, 269–317. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_8.
Texto completoElakya, R., S. Surya, Abinaya G (c1edcca3-9bd8-40f6-a0f6-da223586be33, S. Shanthana y T. Manoranjitham. "Unveiling the Depths". En Advances in Environmental Engineering and Green Technologies, 279–92. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6670-7.ch013.
Texto completoActas de conferencias sobre el tema "Hidden Markov models indexed by trees"
Jin, Shaohua, Yongxue Wang, Huitao Liu, Ying Tian y Hui Li. "Some Strong Limit Theorems for Hidden Markov Models Indexed by a Non-homogeneous Tree". En 2010 Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI). IEEE, 2010. http://dx.doi.org/10.1109/iitsi.2010.68.
Texto completoMilone, Diego H., Diego R. Tomassi y Leandro E. Di Persia. "Signal denoising with hidden Markov models using hidden Markov trees as observation densities". En 2008 IEEE Workshop on Signal Processing for Machine Learning. IEEE, 2008. http://dx.doi.org/10.1109/mlsp.2008.4685509.
Texto completoLacey, Arron, Jingjing Deng y Xianghua Xie. "Protein classification using Hidden Markov models and randomised decision trees". En 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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