TY  - BOOK
AU  - Kaburlasos,Vassilis G.
AU  - Ritter,Gerhard X.
ED  - SpringerLink (Online service)
TI  - Computational Intelligence Based on Lattice Theory
T2  - Studies in Computational Intelligence,
SN  - 9783540726876
AV  - TA329-348 
U1  - 519 23
PY  - 2007///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Engineering mathematics
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Artificial Intelligence (incl. Robotics)
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-540-72687-6
ER  -