scholarly journals Ancestry Inference in Complex Admixtures via Variable-length Markov Chain Linkage Models

2013 ◽  
Vol 20 (3) ◽  
pp. 199-211 ◽  
Author(s):  
Jesse M. Rodriguez ◽  
Sivan Bercovici ◽  
Megan Elmore ◽  
Serafim Batzoglou
Keyword(s):  
Markov Chain ◽  
Variable Length ◽  
Ancestry Inference ◽  
Linkage Models

Optimal Variable Length Markov Chain (VLMC) Modeling of Fading Channels

2006 ◽  
Author(s):  
Wuttipong Kumwilaisak ◽  
C.-c. Kuo ◽  
Dapeng Wu
Keyword(s):  
Markov Chain ◽  
Fading Channels ◽  
Variable Length

Variable Length Markov Chain with Exogenous Covariates

2021 ◽  
Author(s):  
Adriano Zanin Zambom ◽  
Seonjin Kim ◽  
Nancy Lopes Garcia
Keyword(s):  
Markov Chain ◽  
Variable Length

scholarly journals An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

2020 ◽  
Vol 8 (5) ◽  
pp. 5039-5045
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Markov Chain Model ◽  
Variable Length ◽  
Web Pages ◽  
Chain Model ◽  
Web Page ◽  
Semantic Variable

Semantic Variable Length Markov Chain Length Model (SVLMC) is a web page recommendation system which combined the fields of semantic web and web usage mining by the Markov transition probability matrix with rich semantic information extracted from web pages. Though it has high prediction accuracy, it has problem of high state space complexity. The high space complexity reduce the execution speed and reduce the performance of the system, which was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) model that provides high user satisfied recommendation and Confidence Pruned Markov Model (CPMM). The time consumption of CPMM was reduced by Support Vector Machine (SVM). But still the recommendation accuracy is still below the user satisfaction. So in this paper, quickest change detection using Kullback-Leibler Divergence method is introduced to improve the accuracy of recommendation generation by developing a scalable quickest change detection schemes that can be implemented recursively in a more complicated scenario of Markov model and it is included in the training data of SVM. Then the performance of web page recommendation is improved by ranking the web pages using page ranking technique. Thus the performance of web page recommendation generation system has been improved. The experiments are conducted to prove the effectiveness of the proposed work in terms of prediction accuracy, precision, recall, F1-measure, coverage and R measure.

Semantically Enriched Variable Length Markov Chain Model for Analysis of User Web Navigation Sessions

2014 ◽  
Vol 13 (04) ◽  
pp. 721-753 ◽  
Author(s):  
Suresh Shirgave ◽  
Prakash Kulkarni ◽  
José Borges
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Semantic Information ◽  
Markov Models ◽  
Markov Chain Model ◽  
Higher Order ◽  
Variable Length ◽  
Chain Model ◽  
Proposed Model ◽  
Better Than

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 that the method is able to enhance the prediction accuracy relatively to usage-based higher order Markov models and to semantic higher order Markov models based on ontology of concepts. In addition, the proposed model is able to handle the problem of ambiguous predictions. An extensive experimental evaluation was conducted on two real-world data sets and on one partially generated data set. The results show that the proposed model is able to achieve 15–20% better accuracy than the usage-based Markov model, 8–15% better than the semantic ontology Markov model and 7–12% better than semantic-pruned Selective Markov Model. In summary, the SVLMC is the first work proposing the integration of a rich set of detailed semantic information into higher order Web usage Markov models and experimental results reveal that the inclusion of detailed semantic data enhances the prediction ability of Markov models.

scholarly journals Fading Channel Modeling via Variable-Length Markov Chain Technique

2008 ◽  
Vol 57 (3) ◽  
pp. 1338-1358 ◽  
Author(s):  
W. Kumwilaisak ◽  
C.-C.J. Kuo ◽  
Dapeng Wu
Keyword(s):  
Markov Chain ◽  
Fading Channel ◽  
Channel Modeling ◽  
Variable Length
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