Computational Intelligence Based on Lattice Theory

  • Vassilis G. Kaburlasos
  • Gerhard X. Ritter

Part of the Studies in Computational Intelligence book series (SCI, volume 67)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Neural Computation

    1. Front Matter
      Pages 2-2
    2. Gerhard X. Ritter, Gonzalo Urcid
      Pages 25-44
    3. Angelos Barmpoutis, Gerhard X. Ritter
      Pages 45-58
    4. Michael J. Healy, Thomas P. Caudell
      Pages 59-77
  3. Mathematical Morphology Applications

    1. Front Matter
      Pages 80-80
    2. Manuel Graña, Ivan Villaverde, Ramon Moreno, Francisco X. Albizuri
      Pages 101-128
    3. Valérie De Witte, Stefan Schulte, Mike Nachtegael, Tom Mélange, Etienne E. Kerre
      Pages 129-148
  4. Machine Learning Applications

    1. Front Matter
      Pages 174-174
    2. Vassilios Petridis, Vassilis Syrris
      Pages 195-214
    3. J. A. Piedra-Fernández, M. Cantón-Garbín, F. Guindos-Rojas
      Pages 215-232
    4. Ahmad Al-Daraiseh, Assem Kaylani, Michael Georgiopoulos, Mansooreh Mollaghasemi, Annie S. Wu, Georgios Anagnostopoulos
      Pages 233-262
  5. Logic and Inference

    1. Front Matter
      Pages 286-286
    2. Susana Munoz-Hernandez, Claudio Vaucheret
      Pages 287-308
    3. Anestis G. Hatzimichailidis, Basil K. Papadopoulos
      Pages 325-339
    4. Athanasios Kehagias
      Pages 341-360
    5. Athanasios Kehagias
      Pages 361-370
  6. Back Matter
    Pages 371-375

About this book


The emergence of lattice theory within the field of computational intelligence (CI) is partially due to its proven effectiveness in neural computation. Moreover, lattice theory has the potential to unify a number of diverse concepts and aid in the cross-fertilization of both tools and ideas within the numerous subfields of CI. The compilation of this eighteen-chapter book is an initiative towards proliferating established knowledge in the hope to further expand it. This edited book is a balanced synthesis of four parts emphasizing, in turn, neural computation, mathematical morphology, machine learning, and (fuzzy) inference/logic. The articles here demonstrate how lattice theory may suggest viable alternatives in practical clustering, classification, pattern analysis, and regression applications.


Computational Intelligence Fuzzy Lattice Theory Prolog architecture cognition construction fuzzy set fuzzy sets intelligence learning machine learning neural network pattern analysis pattern recognition

Editors and affiliations

  • Vassilis G. Kaburlasos
    • 1
  • Gerhard X. Ritter
    • 2
  1. 1.Technological Educational Institution of KavalaKavalaGreece
  2. 2.University of FloridaGainesvilleUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-540-72686-9
  • Online ISBN 978-3-540-72687-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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