© 2006

Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory

Computational Intelligence and Soft Computing Applications


  • Presents novel tools and useful perspectives for effective pattern classification and function approximation problems based on disparate types of data

  • Introduces useful novel tools, which have the potential to cross-fertilize various techniques in Computational Intelligence /Soft Computing /Machine Learning applications


Part of the Studies in Computational Intelligence book series (SCI, volume 27)

Table of contents

  1. Front Matter
    Pages I-XXII
  2. The Context

    1. Front Matter
      Pages 1-1
    2. Vassilis G. Kaburlasos
      Pages 3-4
    3. Vassilis G. Kaburlasos
      Pages 5-18
  3. Theory and Algorithms

    1. Front Matter
      Pages 19-19
    2. Vassilis G. Kaburlasos
      Pages 21-34
    3. Vassilis G. Kaburlasos
      Pages 35-62
    4. Vassilis G. Kaburlasos
      Pages 63-66
    5. Vassilis G. Kaburlasos
      Pages 67-70
  4. Applications and Comparisons

    1. Front Matter
      Pages 95-95
    2. Vassilis G. Kaburlasos
      Pages 97-122
    3. Vassilis G. Kaburlasos
      Pages 123-140
    4. Vassilis G. Kaburlasos
      Pages 141-172
  5. Conclusion

    1. Front Matter
      Pages 173-173
    2. Vassilis G. Kaburlasos
      Pages 175-178
    3. Vassilis G. Kaburlasos
      Pages 179-182
  6. Back Matter
    Pages 184-245

About this book


By ‘model’ we mean a mathematical description of a world aspect. With the proliferation of computers a variety of modeling paradigms emerged under computational intelligence and soft computing. An advancing technology is currently fragmented due, as well, to the need to cope with different types of data in different application domains. This research monograph proposes a unified, cross-fertilizing approach for knowledge-representation and modeling based on lattice theory. The emphasis is on clustering, classification, and regression applications. It is shown how rigorous analysis and design can be pursued in soft computing using conventional (hard computing) methods. Moreover, non-Turing computation can be pursued. The material here is multi-disciplinary based on our on-going research published in major scientific journals and conferences. Experimental results by various algorithms are demonstrated extensively. Relevant work by other authors is also presented both extensively and comparatively.


Graph algorithms classification computational intelligence knowledge knowledge representation modeling

Authors and affiliations

  1. 1.Department of Industrial InformaticsDivision of Computing Systems, Technological Educational Institution of Kavala65404KavalaGreece

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