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Parallel Factorised Algorithms for Mixture Estimation

  • Milan Tichý
  • Bohumil Kovář
Conference paper

Abstract

This paper describes software aspects of an advisory system based on finite-mixture estimation. Factorised algorithms have been designed. Parallelism is used as a principal approach to acceleration of learning and processing phases. A coarse grain granularity parallelism is used in the first phase of the work. The Parallel Virtual Machine (PVM) is used for parallel implementation.

Keywords

Sequential Algorithm Recursive Little Square Factorise Algorithm Recursive Little Square Algorithm Parallel Virtual Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    A. Geist et al, “PVM: Parallel Virtual Machine”, A User’s Guide and Tutorial tor Networked Parallel Computing, The MIT Press, Cambridge, Massachusetts, 1994.MATHGoogle Scholar
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    M. Kárný et al, “ProDaCTool — theory, algorithms, and software”, EU Project IST ProDaC Tool No. IST-1999-12058, Research Report, 2000.Google Scholar
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    M. Moonen, “Introduction to Adaptive Signal Processing”, K.U. Leuven, Leuven, Belgium, 1999.Google Scholar
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    P. Nedoma et al, “MixTools, MATLAB Toolbox for Mixtures”, EU Project IST ProDaCTool No. IST-1999-12058, Research Report, 2000.Google Scholar
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    P. Tvrdik, “Parallel Systems and Algorithms”, Lecture Notes, Czech Technical University, August 1996.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Milan Tichý
  • Bohumil Kovář
    • 1
  1. 1.UTIA, AV ČRPrague 8Czech Republic

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