Items related to Discrete Characteristic Probability Distribution Theorem

Discrete Characteristic Probability Distribution Theorem - Softcover

 
9783639515701: Discrete Characteristic Probability Distribution Theorem

Synopsis

As internet has become a crucial element of Information Technology in our daily lives, so has the web search engine. Web search engines are formed with the reason to search information from the World Wide Web (WWW). This work proposes a new approach to rank web pages for search engines. This new method is an alternative to PageRank algorithm. It starts off by discussing in brief about the algorithm of PageRank, the mathematical background of PageRank and its role to rank web pages. Next, the book introduces a new method and formula to find the probability that can be used to rank web pages efficiently. The derivation of formula and how it is found is explained in details. The proposed methodology shows how it is not only dependent on backward link while ranking webpages. It also considers other entities of a webpage to rank a webpage. The data and results for this research are presented to prove how the new probability formula derived is efficient to overcome the backward overloading problem of PageRank.

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  • PublisherScholars' Press
  • Publication date2015
  • ISBN 10 3639515706
  • ISBN 13 9783639515701
  • BindingPaperback
  • Number of pages72

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -As internet has become a crucial element of Information Technology in our daily lives, so has the web search engine. Web search engines are formed with the reason to search information from the World Wide Web (WWW). This work proposes a new approach to rank web pages for search engines. This new method is an alternative to PageRank algorithm. It starts off by discussing in brief about the algorithm of PageRank, the mathematical background of PageRank and its role to rank web pages. Next, the book introduces a new method and formula to find the probability that can be used to rank web pages efficiently. The derivation of formula and how it is found is explained in details. The proposed methodology shows how it is not only dependent on backward link while ranking webpages. It also considers other entities of a webpage to rank a webpage. The data and results for this research are presented to prove how the new probability formula derived is efficient to overcome the backward overloading problem of PageRank. 72 pp. Englisch. Seller Inventory # 9783639515701

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As internet has become a crucial element of Information Technology in our daily lives, so has the web search engine. Web search engines are formed with the reason to search information from the World Wide Web (WWW). This work proposes a new approach to rank web pages for search engines. This new method is an alternative to PageRank algorithm. It starts off by discussing in brief about the algorithm of PageRank, the mathematical background of PageRank and its role to rank web pages. Next, the book introduces a new method and formula to find the probability that can be used to rank web pages efficiently. The derivation of formula and how it is found is explained in details. The proposed methodology shows how it is not only dependent on backward link while ranking webpages. It also considers other entities of a webpage to rank a webpage. The data and results for this research are presented to prove how the new probability formula derived is efficient to overcome the backward overloading problem of PageRank. Seller Inventory # 9783639515701

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chowdhury Adib KabirAdib Kabir Chowdhury has obtained his BTech (Computer Systems & Networking) and MPhil. (Electrical & Computer Engineering) degree from Curtin University, Sarawak, Malaysia. Currently he is working as a Lecturer in. Seller Inventory # 17682744

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