Computational Intelligence Based on Lattice Theory

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 t...

Full description

Corporate Author: SpringerLink (Online service)
Other Authors: Kaburlasos, Vassilis G. (Editor), Ritter, Gerhard X. (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2007, 2007
Edition:1st ed. 2007
Series:Studies in Computational Intelligence
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 03368nmm a2200337 u 4500
001 EB000378796
003 EBX01000000000000000231848
005 00000000000000.0
007 cr|||||||||||||||||||||
008 130626 ||| eng
020 |a 9783540726876 
100 1 |a Kaburlasos, Vassilis G.  |e [editor] 
245 0 0 |a Computational Intelligence Based on Lattice Theory  |h Elektronische Ressource  |c edited by Vassilis G. Kaburlasos, Gerhard X. Ritter 
250 |a 1st ed. 2007 
260 |a Berlin, Heidelberg  |b Springer Berlin Heidelberg  |c 2007, 2007 
300 |a XVI, 375 p  |b online resource 
505 0 |a Neural Computation -- Granular Enhancement of Fuzzy ART/SOM Neural Classifiers Based on Lattice Theory -- Learning in Lattice Neural Networks that Employ Dendritic Computing -- Orthonormal Basis Lattice Neural Networks -- Generalized Lattices Express Parallel Distributed Concept Learning -- Mathematical Morphology Applications -- Noise Masking for Pattern Recall Using a Single Lattice Matrix Associative Memory -- Convex Coordinates From Lattice Independent Sets for Visual Pattern Recognition -- A Lattice-Based Approach to Mathematical Morphology for Greyscale and Colour Images -- Morphological and Certain Fuzzy Morphological Associative Memories for Classification and Prediction -- Machine Learning Applications -- The Fuzzy Lattice Reasoning (FLR) Classifier for Mining Environmental Data -- Machine Learning Techniques for Environmental Data Estimation -- Application of Fuzzy Lattice Neurocomputing (FLN) in Ocean Satellite Images for Pattern Recognition -- Genetically Engineered ART Architectures -- Fuzzy Lattice Reasoning (FLR) Classification Using Similarity Measures -- Logic and Inference -- Fuzzy Prolog: Default Values to Represent Missing Information -- Valuations on Lattices: Fuzzification and its Implications -- L-fuzzy Sets and Intuitionistic Fuzzy Sets -- A Family of Multi-valued t-norms and t-conorms -- The Construction of Fuzzy-valued t-norms and t-conorms 
653 |a Applied mathematics 
653 |a Engineering mathematics 
653 |a Artificial Intelligence 
653 |a Mathematical and Computational Engineering 
653 |a Artificial intelligence 
700 1 |a Ritter, Gerhard X.  |e [editor] 
710 2 |a SpringerLink (Online service) 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
490 0 |a Studies in Computational Intelligence 
856 |u https://doi.org/10.1007/978-3-540-72687-6?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 006.3 
520 |a 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