Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables

In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is provided to decide under which conditions IMBT outper...

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Autores principales: Finta, István, Szénási, Sándor, Farkas, Lóránt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516675/
https://www.ncbi.nlm.nih.gov/pubmed/33286019
http://dx.doi.org/10.3390/e22020245
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author Finta, István
Szénási, Sándor
Farkas, Lóránt
author_facet Finta, István
Szénási, Sándor
Farkas, Lóránt
author_sort Finta, István
collection PubMed
description In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is provided to decide under which conditions IMBT outperforms other data structures typically used in the field, as a function of the statistical characteristics of the commonly occurring anomalies in the arrival of data packets. We use in the modeling Bernstein theorem, Markov property, Fibonacci sequences, bipartite multi-graphs, and contingency tables.
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spelling pubmed-75166752020-11-09 Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables Finta, István Szénási, Sándor Farkas, Lóránt Entropy (Basel) Article In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is provided to decide under which conditions IMBT outperforms other data structures typically used in the field, as a function of the statistical characteristics of the commonly occurring anomalies in the arrival of data packets. We use in the modeling Bernstein theorem, Markov property, Fibonacci sequences, bipartite multi-graphs, and contingency tables. MDPI 2020-02-21 /pmc/articles/PMC7516675/ /pubmed/33286019 http://dx.doi.org/10.3390/e22020245 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Finta, István
Szénási, Sándor
Farkas, Lóránt
Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables
title Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables
title_full Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables
title_fullStr Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables
title_full_unstemmed Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables
title_short Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables
title_sort input pattern classification based on the markov property of the imbt with related equations and contingency tables
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516675/
https://www.ncbi.nlm.nih.gov/pubmed/33286019
http://dx.doi.org/10.3390/e22020245
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