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Relevant bibliographies by topics / Variable Order Markov Model

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Journal articles

Academic literature on the topic 'Variable Order Markov Model'

Author: Grafiati

Published: 9 March 2023

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Journal articles on the topic "Variable Order Markov Model"

1

Shirgave, Suresh, Prakash Kulkarni, and José Borges. "Semantically Enriched Variable Length Markov Chain Model for Analysis of User Web Navigation Sessions." International Journal of Information Technology & Decision Making 13, no. 04 (2014): 721–53. http://dx.doi.org/10.1142/s0219622014500643.

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The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show th

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2

Begleiter, R., R. El-Yaniv, and G. Yona. "On Prediction Using Variable Order Markov Models." Journal of Artificial Intelligence Research 22 (December 1, 2004): 385–421. http://dx.doi.org/10.1613/jair.1491.

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This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to

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3

Tengke Xiong, Shengrui Wang, Qingshan Jiang, and Joshua Zhexue Huang. "A Novel Variable-order Markov Model for Clustering Categorical Sequences." IEEE Transactions on Knowledge and Data Engineering 26, no. 10 (2014): 2339–53. http://dx.doi.org/10.1109/tkde.2013.104.

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Cunial, Fabio, Jarno Alanko, and Djamal Belazzougui. "A framework for space-efficient variable-order Markov models." Bioinformatics 35, no. 22 (2019): 4607–16. http://dx.doi.org/10.1093/bioinformatics/btz268.

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Abstract Motivation Markov models with contexts of variable length are widely used in bioinformatics for representing sets of sequences with similar biological properties. When models contain many long contexts, existing implementations are either unable to handle genome-scale training datasets within typical memory budgets, or they are optimized for specific model variants and are thus inflexible. Results We provide practical, versatile representations of variable-order Markov models and of interpolated Markov models, that support a large number of context-selection criteria, scoring function

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POSCH, STEFAN, JAN GRAU, ANDRE GOHR, IRAD BEN-GAL, ALEXANDER E. KEL, and IVO GROSSE. "RECOGNITION OF CIS-REGULATORY ELEMENTS WITH VOMBAT." Journal of Bioinformatics and Computational Biology 05, no. 02b (2007): 561–77. http://dx.doi.org/10.1142/s0219720007002886.

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Variable order Markov models and variable order Bayesian trees have been proposed for the recognition of cis-regulatory elements, and it has been demonstrated that they outperform traditional models such as position weight matrices, Markov models, and Bayesian trees for the recognition of binding sites in prokaryotes. Here, we study to which degree variable order models can improve the recognition of eukaryotic cis-regulatory elements. We find that variable order models can improve the recognition of binding sites of all the studied transcription factors. To ease a systematic evaluation of dif

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Xiao Li, Yuhao Wang, and Yuan Liu. "A Channel Cognitive Method for Local Fading Characteristics using Variable-Order Markov Model." Journal of Communications and Information Sciences 1, no. 2 (2011): 1–12. http://dx.doi.org/10.4156/jcis.vol1.issue2.1.

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Saadani, A., P. Gelpi, and P. Tortelier. "A Variable-Order Markov-Chain-Based Model for Rayleigh Fading and Rake Receiver." IEEE Signal Processing Letters 11, no. 3 (2004): 356–58. http://dx.doi.org/10.1109/lsp.2003.822915.

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Qi, Zhang, Wen Guang, Chen Zhixin, et al. "Contact stress reliability analysis based on first order second moment for variable hyperbolic circular arc gear." Advances in Mechanical Engineering 14, no. 7 (2022): 168781322211112. http://dx.doi.org/10.1177/16878132221111210.

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Aiming at the contact strength reliability of variable hyperbolic circular arc gear, a reliability analysis method for contact strength of variable hyperbolic circular arc gear based on Kriging model and advanced first-order and second-moment algorithm is proposed. Kriging model was used to establish the limit state equation of the contact stress reliability analysis of variable hyperbolic circular arc gear, and the advanced first-order second-moment method was used to analyze the contact stress reliability of variable hyperbolic circular arc gear based on the limit state equation of the conta

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Melikov, A. Z., L. A. Ponomarenko, and S. A. Bagirova. "Markov Models of Queueing–Inventory Systems with Variable Order Size." Cybernetics and Systems Analysis 53, no. 3 (2017): 373–86. http://dx.doi.org/10.1007/s10559-017-9937-3.

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Kohli, Amit Kumar, Amrita Rai, and Meher Krishna Patel. "Variable Forgetting Factor LS Algorithm for Polynomial Channel Model." ISRN Signal Processing 2011 (December 30, 2011): 1–4. http://dx.doi.org/10.5402/2011/915259.

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Variable forgetting factor (VFF) least squares (LS) algorithm for polynomial channel paradigm is presented for improved tracking performance under nonstationary environment. The main focus is on updating VFF when each time-varying fading channel is considered to be a first-order Markov process. In addition to efficient tracking under frequency-selective fading channels, the incorporation of proposed numeric variable forgetting factor (NVFF) in LS algorithm reduces the computational complexity.

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