Advanced
Advanced

Browsing by Author Le, Trung Kien

Jump to: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
or enter first few letters:  
Showing results 1 to 2 of 2
  • 171.pdf.jpg
  • Article

  • Applying probabilistic model for ranking Webs in multi-context

    • Authors: Le, Trung Kien; Tran, Loc Hung; Le, Anh Vu (2007)

  • The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by applying probabilistic model on the link structure of Webs to evaluate the “importance” of Webs. In PageRank probabilistic niodel, the links and webs are uniform, so the rank score of vvebs are quite independent from their content. In practice, the researchers often hope that the web results can be ranked by their proposed topics. Moreover, when computer’s techniques solve given problems ineffectively, it*s necessary to do better research in theoretical problems. From this judgement, in this paper, we introduce and describe the MPageRank based on a nevv probabilistic model supporti...
  • 161.pdf.jpg
  • Article

  • Deeper Inside Finite-state Markov chains

    • Authors: Le, Trung Kien; Le, Trung Hieu; Tran, Loc Hung; Nguyen, Duy Tien (2007)

  • The effective application of Markov chains has been paid much attention, and it has raised a lot of thcoretical and applied problems. In this paper, we would like to approach One of these problems which is finding the long-run behavior of extremely huge-state Markov chains according to the direction of investigating the structure of Markov Graph to reduce complexity of computation. We focus on the way to access to the finite-state Markov chain theory via Graph theory. We suggested some basic knowledge about state classification and a small project of modelling the structure and the moving process of the finite-state Markov chain model. This project based on the remark that it is...

    Readership Map

    ABSTRACTS VIEWS

    45535425

    VIEWS & DOWNLOAD

    27616500

    Content Distribution

Browsing by Author Le, Trung Kien

Jump to: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
or enter first few letters:  
Showing results 1 to 2 of 2
  • 171.pdf.jpg
  • Article

  • Applying probabilistic model for ranking Webs in multi-context

    • Authors: Le, Trung Kien; Tran, Loc Hung; Le, Anh Vu (2007)

  • The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by applying probabilistic model on the link structure of Webs to evaluate the “importance” of Webs. In PageRank probabilistic niodel, the links and webs are uniform, so the rank score of vvebs are quite independent from their content. In practice, the researchers often hope that the web results can be ranked by their proposed topics. Moreover, when computer’s techniques solve given problems ineffectively, it*s necessary to do better research in theoretical problems. From this judgement, in this paper, we introduce and describe the MPageRank based on a nevv probabilistic model supporti...
  • 161.pdf.jpg
  • Article

  • Deeper Inside Finite-state Markov chains

    • Authors: Le, Trung Kien; Le, Trung Hieu; Tran, Loc Hung; Nguyen, Duy Tien (2007)

  • The effective application of Markov chains has been paid much attention, and it has raised a lot of thcoretical and applied problems. In this paper, we would like to approach One of these problems which is finding the long-run behavior of extremely huge-state Markov chains according to the direction of investigating the structure of Markov Graph to reduce complexity of computation. We focus on the way to access to the finite-state Markov chain theory via Graph theory. We suggested some basic knowledge about state classification and a small project of modelling the structure and the moving process of the finite-state Markov chain model. This project based on the remark that it is...