Overview
- Editors:
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Vassilis G. Kaburlasos
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Technological Educational Institution of Kavala, Kavala, Greece
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Gerhard X. Ritter
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University of Florida, Gainesville, USA
- Resent results of Computational Intelligence Based on Lattice Theory
- Outcome of a special session held during in WCCI 2006
- Includes supplementary material: sn.pub/extras
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About this book
A number of di?erent instruments for design can be uni?ed in the context of lattice theory towards cross-fertilization By“latticetheory”[1]wemean,equivalently,eitherapartialordering relation [2,3]ora couple of binary algebraic operations [3, 4]. There is a growing interest in computational intelligence based on lattice theory. A number of researchers are currently active developing lattice theory based models and techniques in engineering, computer and information s- ences, applied mathematics, and other scienti?c endeavours. Some of these models and techniques are presented here. However, currently, lattice theory is not part of the mainstream of com- tationalintelligence.Amajorreasonforthisisthe“learningcurve”associated with novel notions and tools. Moreover, practitioners of lattice theory, in s- ci?c domains of interest, frequently develop their own tools and/or practices without being aware of valuable contributions made by colleagues. Hence, (potentially) useful work may be ignored, or duplicated. Yet, other times, di?erent authors may introduce a con?icting terminology. The compilation of this book is an initiative towards proliferating est- lished knowledge in the hope to further expand it, soundly. There was a critical mass of people and ideas engaged to produce this book. Around two thirds of this book’s chapters are substantial enhancements of preliminary works presented lately in a three-part special session entitled “Computational Intelligence Based on Lattice Theory” organized in the c- text of the World Congress in Computational Intelligence (WCCI), FUZZ- IEEE program, July 16-21, 2006 in Vancouver, BC, Canada. The remaining book chapters are novel contributions by other researchers.
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Table of contents (18 chapters)
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Neural Computation
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- Gerhard X. Ritter, Gonzalo Urcid
Pages 25-44
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- Angelos Barmpoutis, Gerhard X. Ritter
Pages 45-58
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- Michael J. Healy, Thomas P. Caudell
Pages 59-77
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Mathematical Morphology Applications
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- Gonzalo Urcid, Gerhard X. Ritter
Pages 81-100
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- Manuel Graña, Ivan Villaverde, Ramon Moreno, Francisco X. Albizuri
Pages 101-128
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- Valérie De Witte, Stefan Schulte, Mike Nachtegael, Tom Mélange, Etienne E. Kerre
Pages 129-148
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- Peter Sussner, Marcos Eduardo Valle
Pages 149-171
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Machine Learning Applications
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Front Matter
Pages 174-174
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- Vassilios Petridis, Vassilis Syrris
Pages 195-214
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- J. A. Piedra-Fernández, M. Cantón-Garbín, F. Guindos-Rojas
Pages 215-232
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- Ahmad Al-Daraiseh, Assem Kaylani, Michael Georgiopoulos, Mansooreh Mollaghasemi, Annie S. Wu, Georgios Anagnostopoulos
Pages 233-262
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Logic and Inference
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Front Matter
Pages 286-286
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- Susana Munoz-Hernandez, Claudio Vaucheret
Pages 287-308
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Editors and Affiliations
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Technological Educational Institution of Kavala, Kavala, Greece
Vassilis G. Kaburlasos
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University of Florida, Gainesville, USA
Gerhard X. Ritter